{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest Cover Machine Hack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sravan VG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\fastai-cpu\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.ensemble.forest module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.ensemble. Anything that cannot be imported from sklearn.ensemble is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "from pandas_summary import DataFrameSummary\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from IPython.display import display\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_plot_sizes(12,14,16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load in our data from last lesson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"..\"\n",
    "# import feather\n",
    "# df_raw = feather.read_dataframe('tmp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_val = []\n",
    "df_train = pd.read_csv(f'{PATH}\\Train.csv', low_memory=False,na_values = na_val)\n",
    "df_test = pd.read_csv(f'{PATH}\\Test.csv', low_memory=False,na_values = na_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29050"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>Cover_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "      <td>29050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2959.328330</td>\n",
       "      <td>155.720241</td>\n",
       "      <td>14.028537</td>\n",
       "      <td>269.220792</td>\n",
       "      <td>46.332737</td>\n",
       "      <td>2346.340241</td>\n",
       "      <td>212.042134</td>\n",
       "      <td>223.446850</td>\n",
       "      <td>142.854630</td>\n",
       "      <td>1971.574871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091738</td>\n",
       "      <td>0.078967</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.027539</td>\n",
       "      <td>0.023821</td>\n",
       "      <td>0.014561</td>\n",
       "      <td>5.536110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>277.578227</td>\n",
       "      <td>112.109417</td>\n",
       "      <td>7.458200</td>\n",
       "      <td>212.406921</td>\n",
       "      <td>58.938186</td>\n",
       "      <td>1558.148732</td>\n",
       "      <td>26.678889</td>\n",
       "      <td>19.610671</td>\n",
       "      <td>38.000582</td>\n",
       "      <td>1321.145310</td>\n",
       "      <td>...</td>\n",
       "      <td>0.288661</td>\n",
       "      <td>0.269692</td>\n",
       "      <td>0.052078</td>\n",
       "      <td>0.055267</td>\n",
       "      <td>0.014370</td>\n",
       "      <td>0.019456</td>\n",
       "      <td>0.163650</td>\n",
       "      <td>0.152494</td>\n",
       "      <td>0.119790</td>\n",
       "      <td>1.438261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1879.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-166.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2809.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1103.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2995.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3161.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>3329.750000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2536.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3844.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>7087.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>7142.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Elevation(meters)  Aspect(degrees)  Slope(degrees)  \\\n",
       "count       29050.000000     29050.000000    29050.000000   \n",
       "mean         2959.328330       155.720241       14.028537   \n",
       "std           277.578227       112.109417        7.458200   \n",
       "min          1879.000000         0.000000        0.000000   \n",
       "25%          2809.000000        58.000000        9.000000   \n",
       "50%          2995.000000       128.000000       13.000000   \n",
       "75%          3161.000000       262.000000       18.000000   \n",
       "max          3844.000000       360.000000       61.000000   \n",
       "\n",
       "       Horizontal_Distance_To_Hydrology(meters)  \\\n",
       "count                              29050.000000   \n",
       "mean                                 269.220792   \n",
       "std                                  212.406921   \n",
       "min                                    0.000000   \n",
       "25%                                  108.000000   \n",
       "50%                                  218.000000   \n",
       "75%                                  390.000000   \n",
       "max                                 1320.000000   \n",
       "\n",
       "       Vertical_Distance_To_Hydrology(meters)  \\\n",
       "count                            29050.000000   \n",
       "mean                                46.332737   \n",
       "std                                 58.938186   \n",
       "min                               -166.000000   \n",
       "25%                                  7.000000   \n",
       "50%                                 29.000000   \n",
       "75%                                 69.000000   \n",
       "max                                589.000000   \n",
       "\n",
       "       Horizontal_Distance_To_Roadways(meters)  Hillshade_9am  Hillshade_Noon  \\\n",
       "count                             29050.000000   29050.000000    29050.000000   \n",
       "mean                               2346.340241     212.042134      223.446850   \n",
       "std                                1558.148732      26.678889       19.610671   \n",
       "min                                   0.000000      58.000000       63.000000   \n",
       "25%                                1103.000000     198.000000      213.000000   \n",
       "50%                                1984.000000     218.000000      226.000000   \n",
       "75%                                3329.750000     231.000000      237.000000   \n",
       "max                                7087.000000     254.000000      254.000000   \n",
       "\n",
       "       Hillshade_3pm  Horizontal_Distance_To_Fire_Points(meters)  ...  \\\n",
       "count   29050.000000                                29050.000000  ...   \n",
       "mean      142.854630                                 1971.574871  ...   \n",
       "std        38.000582                                 1321.145310  ...   \n",
       "min         0.000000                                    0.000000  ...   \n",
       "25%       120.000000                                 1020.000000  ...   \n",
       "50%       143.000000                                 1704.000000  ...   \n",
       "75%       168.000000                                 2536.000000  ...   \n",
       "max       251.000000                                 7142.000000  ...   \n",
       "\n",
       "       Soil_Type_32  Soil_Type_33  Soil_Type_34  Soil_Type_35  Soil_Type_36  \\\n",
       "count  29050.000000  29050.000000  29050.000000  29050.000000  29050.000000   \n",
       "mean       0.091738      0.078967      0.002719      0.003064      0.000207   \n",
       "std        0.288661      0.269692      0.052078      0.055267      0.014370   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       Soil_Type_37  Soil_Type_38  Soil_Type_39  Soil_Type_40    Cover_Type  \n",
       "count  29050.000000  29050.000000  29050.000000  29050.000000  29050.000000  \n",
       "mean       0.000379      0.027539      0.023821      0.014561      5.536110  \n",
       "std        0.019456      0.163650      0.152494      0.119790      1.438261  \n",
       "min        0.000000      0.000000      0.000000      0.000000      1.000000  \n",
       "25%        0.000000      0.000000      0.000000      0.000000      5.000000  \n",
       "50%        0.000000      0.000000      0.000000      0.000000      5.000000  \n",
       "75%        0.000000      0.000000      0.000000      0.000000      7.000000  \n",
       "max        1.000000      1.000000      1.000000      1.000000      7.000000  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw_shuff = df_train.sample(n = len(df_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_raw_shuff\n",
    "# display_all(df_raw_shuff[-200:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation(meters)', 'Aspect(degrees)', 'Slope(degrees)',\n",
       "       'Horizontal_Distance_To_Hydrology(meters)',\n",
       "       'Vertical_Distance_To_Hydrology(meters)',\n",
       "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
       "       'Hillshade_Noon', 'Hillshade_3pm',\n",
       "       'Horizontal_Distance_To_Fire_Points(meters)', 'Wilderness_Area_1',\n",
       "       'Wilderness_Area_2', 'Wilderness_Area_3', 'Wilderness_Area_4',\n",
       "       'Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
       "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
       "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
       "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
       "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
       "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
       "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
       "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
       "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
       "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40',\n",
       "       'Cover_Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>Cover_Type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12243</th>\n",
       "      <td>2492</td>\n",
       "      <td>144</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>-11</td>\n",
       "      <td>1736</td>\n",
       "      <td>226</td>\n",
       "      <td>239</td>\n",
       "      <td>147</td>\n",
       "      <td>499</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>2532</td>\n",
       "      <td>351</td>\n",
       "      <td>8</td>\n",
       "      <td>408</td>\n",
       "      <td>159</td>\n",
       "      <td>957</td>\n",
       "      <td>206</td>\n",
       "      <td>227</td>\n",
       "      <td>159</td>\n",
       "      <td>1366</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25443</th>\n",
       "      <td>2951</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4734</td>\n",
       "      <td>220</td>\n",
       "      <td>229</td>\n",
       "      <td>146</td>\n",
       "      <td>1226</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18108</th>\n",
       "      <td>2744</td>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>390</td>\n",
       "      <td>76</td>\n",
       "      <td>1761</td>\n",
       "      <td>222</td>\n",
       "      <td>235</td>\n",
       "      <td>149</td>\n",
       "      <td>1846</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6650</th>\n",
       "      <td>2864</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>2717</td>\n",
       "      <td>212</td>\n",
       "      <td>215</td>\n",
       "      <td>139</td>\n",
       "      <td>2380</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Elevation(meters)  Aspect(degrees)  Slope(degrees)  \\\n",
       "12243               2492              144               4   \n",
       "2440                2532              351               8   \n",
       "25443               2951               37               5   \n",
       "18108               2744               84               2   \n",
       "6650                2864               20              12   \n",
       "\n",
       "       Horizontal_Distance_To_Hydrology(meters)  \\\n",
       "12243                                        30   \n",
       "2440                                        408   \n",
       "25443                                         0   \n",
       "18108                                       390   \n",
       "6650                                         67   \n",
       "\n",
       "       Vertical_Distance_To_Hydrology(meters)  \\\n",
       "12243                                     -11   \n",
       "2440                                      159   \n",
       "25443                                       0   \n",
       "18108                                      76   \n",
       "6650                                       10   \n",
       "\n",
       "       Horizontal_Distance_To_Roadways(meters)  Hillshade_9am  Hillshade_Noon  \\\n",
       "12243                                     1736            226             239   \n",
       "2440                                       957            206             227   \n",
       "25443                                     4734            220             229   \n",
       "18108                                     1761            222             235   \n",
       "6650                                      2717            212             215   \n",
       "\n",
       "       Hillshade_3pm  Horizontal_Distance_To_Fire_Points(meters)  ...  \\\n",
       "12243            147                                         499  ...   \n",
       "2440             159                                        1366  ...   \n",
       "25443            146                                        1226  ...   \n",
       "18108            149                                        1846  ...   \n",
       "6650             139                                        2380  ...   \n",
       "\n",
       "       Soil_Type_32  Soil_Type_33  Soil_Type_34  Soil_Type_35  Soil_Type_36  \\\n",
       "12243             0             0             0             0             0   \n",
       "2440              0             0             0             0             0   \n",
       "25443             0             0             0             0             0   \n",
       "18108             0             0             0             0             0   \n",
       "6650              0             0             0             0             0   \n",
       "\n",
       "       Soil_Type_37  Soil_Type_38  Soil_Type_39  Soil_Type_40  Cover_Type  \n",
       "12243             0             0             0             0           3  \n",
       "2440              0             0             0             0           6  \n",
       "25443             0             0             0             0           5  \n",
       "18108             0             0             0             0           5  \n",
       "6650              0             0             0             0           7  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Analysis and extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_normalize = ['Aspect(degrees)','Slope(degrees)','Horizontal_Distance_To_Hydrology(meters)','Vertical_Distance_To_Hydrology(meters)',\n",
    "  'Hillshade_9am','Hillshade_Noon','Hillshade_3pm','Horizontal_Distance_To_Fire_Points(meters)']\n",
    "\n",
    "df_train[cols_to_normalize] = normalize(df_train[cols_to_normalize])\n",
    "df_test[cols_to_normalize] = normalize(df_test[cols_to_normalize])\n",
    "\n",
    "\n",
    "feature_cols = [col for col in df_train.columns if col not in ['Cover_Type','Id']]\n",
    "feature_cols.append('binned_elevation')\n",
    "feature_cols.append('Horizontal_Distance_To_Roadways_Log')\n",
    "feature_cols.append('Soil_Type12_32')\n",
    "feature_cols.append('Soil_Type23_22_32_33')\n",
    "\n",
    "\n",
    "df_train['binned_elevation'] = [math.floor(v/50.0) for v in df_train['Elevation(meters)']]\n",
    "df_test['binned_elevation'] = [math.floor(v/50.0) for v in df_test['Elevation(meters)']]\n",
    "\n",
    "df_train['Horizontal_Distance_To_Roadways_Log'] = [math.log(v+1) for v in df_train['Horizontal_Distance_To_Roadways(meters)']]\n",
    "df_test['Horizontal_Distance_To_Roadways_Log'] = [math.log(v+1) for v in df_test['Horizontal_Distance_To_Roadways(meters)']]\n",
    "\n",
    "df_train['Soil_Type12_32'] = df_train['Soil_Type_32'] + df_train['Soil_Type_12']\n",
    "df_test['Soil_Type12_32'] = df_test['Soil_Type_32'] + df_test['Soil_Type_12']\n",
    "df_train['Soil_Type23_22_32_33'] = df_train['Soil_Type_23'] + df_train['Soil_Type_22'] + df_train['Soil_Type_32'] + df_train['Soil_Type_33']\n",
    "df_test['Soil_Type23_22_32_33'] = df_test['Soil_Type_23'] + df_test['Soil_Type_22'] + df_test['Soil_Type_32'] + df_test['Soil_Type_33']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation(meters)', 'Aspect(degrees)', 'Slope(degrees)',\n",
       "       'Horizontal_Distance_To_Hydrology(meters)',\n",
       "       'Vertical_Distance_To_Hydrology(meters)',\n",
       "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
       "       'Hillshade_Noon', 'Hillshade_3pm',\n",
       "       'Horizontal_Distance_To_Fire_Points(meters)', 'Wilderness_Area_1',\n",
       "       'Wilderness_Area_2', 'Wilderness_Area_3', 'Wilderness_Area_4',\n",
       "       'Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
       "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
       "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
       "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
       "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
       "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
       "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
       "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
       "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
       "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40',\n",
       "       'Cover_Type', 'binned_elevation', 'Horizontal_Distance_To_Roadways_Log',\n",
       "       'Soil_Type12_32', 'Soil_Type23_22_32_33'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some More feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_feature(data):   \n",
    "    data['Ele_minus_VDtHyd'] = data['Elevation(meters)']-data['Vertical_Distance_To_Hydrology(meters)']\n",
    "    data['Ele_plus_VDtHyd'] = data['Elevation(meters)']+data['Vertical_Distance_To_Hydrology(meters)']\n",
    "    data['Distanse_to_Hydrolody'] = (data['Horizontal_Distance_To_Hydrology(meters)']**2+data['Vertical_Distance_To_Hydrology(meters)']**2)**0.5\n",
    "    data['Hydro_plus_Fire'] = data['Horizontal_Distance_To_Hydrology(meters)']+data['Horizontal_Distance_To_Fire_Points(meters)']\n",
    "    data['Hydro_minus_Fire'] = data['Horizontal_Distance_To_Hydrology(meters)']-data['Horizontal_Distance_To_Fire_Points(meters)']\n",
    "    data['Hydro_plus_Road'] = data['Horizontal_Distance_To_Hydrology(meters)']+data['Horizontal_Distance_To_Roadways(meters)']\n",
    "    data['Hydro_minus_Road'] = data['Horizontal_Distance_To_Hydrology(meters)']-data['Horizontal_Distance_To_Roadways(meters)']\n",
    "    data['Fire_plus_Road'] = data['Horizontal_Distance_To_Fire_Points(meters)']+data['Horizontal_Distance_To_Roadways(meters)']\n",
    "    data['Fire_minus_Road'] = data['Horizontal_Distance_To_Fire_Points(meters)']-data['Horizontal_Distance_To_Roadways(meters)']\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12243    0.232259\n",
       "2440     0.104837\n",
       "25443    0.114475\n",
       "18108    0.077468\n",
       "6650     0.057816\n",
       "           ...   \n",
       "7842     0.247512\n",
       "24904    0.253573\n",
       "14854    0.059994\n",
       "21300    0.144090\n",
       "17953    0.054028\n",
       "Name: Hillshade_3pm, Length: 29050, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Hillshade_3pm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_feature(df_train);\n",
    "add_feature(df_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation(meters)', 'Aspect(degrees)', 'Slope(degrees)',\n",
       "       'Horizontal_Distance_To_Hydrology(meters)',\n",
       "       'Vertical_Distance_To_Hydrology(meters)',\n",
       "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
       "       'Hillshade_Noon', 'Hillshade_3pm',\n",
       "       'Horizontal_Distance_To_Fire_Points(meters)', 'Wilderness_Area_1',\n",
       "       'Wilderness_Area_2', 'Wilderness_Area_3', 'Wilderness_Area_4',\n",
       "       'Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
       "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
       "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
       "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
       "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
       "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
       "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
       "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
       "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
       "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40',\n",
       "       'binned_elevation', 'Horizontal_Distance_To_Roadways_Log',\n",
       "       'Soil_Type12_32', 'Soil_Type23_22_32_33', 'Ele_minus_VDtHyd',\n",
       "       'Ele_plus_VDtHyd', 'Distanse_to_Hydrolody', 'Hydro_plus_Fire',\n",
       "       'Hydro_minus_Fire', 'Hydro_plus_Road', 'Hydro_minus_Road',\n",
       "       'Fire_plus_Road', 'Fire_minus_Road'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cats(df_train)\n",
    "df_trn, y_trn, nas = proc_df(df_train, 'Cover_Type')\n",
    "df_trn.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_vals(a,n): return a[:n], a[n:]\n",
    "n_valid = 300\n",
    "n_trn = len(df_trn)-n_valid\n",
    "X_train, X_valid = split_vals(df_trn, n_trn)\n",
    "y_train, y_valid = split_vals(y_trn, n_trn)\n",
    "raw_train, raw_valid = split_vals(df_train, n_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, ..., 5, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(x,y): return math.sqrt(((x-y)**2).mean())\n",
    "def logloss(x, y):\n",
    "    y2 = []\n",
    "    for ys in y:\n",
    "        y1 = [0,0,0,0,0,0,0]\n",
    "        y1[ys-1] = 1\n",
    "        y2.append(y1)\n",
    "    \n",
    "#     print(len(y2[0]), x.shape)\n",
    "    y2 = np.array(y2)\n",
    "    return sklearn.metrics.log_loss(y2, x)\n",
    "    \n",
    "\n",
    "def print_score(m):\n",
    "    res = [logloss(m.predict_proba(X_train), y_train), logloss(m.predict_proba(X_valid), y_valid),\n",
    "                m.score(X_train, y_train), m.score(X_valid, y_valid)]\n",
    "    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type23_22_32_33</th>\n",
       "      <th>Ele_minus_VDtHyd</th>\n",
       "      <th>Ele_plus_VDtHyd</th>\n",
       "      <th>Distanse_to_Hydrolody</th>\n",
       "      <th>Hydro_plus_Fire</th>\n",
       "      <th>Hydro_minus_Fire</th>\n",
       "      <th>Hydro_plus_Road</th>\n",
       "      <th>Hydro_minus_Road</th>\n",
       "      <th>Fire_plus_Road</th>\n",
       "      <th>Fire_minus_Road</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50118</th>\n",
       "      <td>2785</td>\n",
       "      <td>0.024883</td>\n",
       "      <td>0.002890</td>\n",
       "      <td>0.038850</td>\n",
       "      <td>0.018943</td>\n",
       "      <td>3090</td>\n",
       "      <td>0.038208</td>\n",
       "      <td>0.038208</td>\n",
       "      <td>0.019586</td>\n",
       "      <td>0.997096</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2784.981057</td>\n",
       "      <td>2785.018943</td>\n",
       "      <td>0.043222</td>\n",
       "      <td>1.035946</td>\n",
       "      <td>-0.958246</td>\n",
       "      <td>3090.038850</td>\n",
       "      <td>-3089.961150</td>\n",
       "      <td>3090.997096</td>\n",
       "      <td>-3089.002904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338704</th>\n",
       "      <td>2265</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.036667</td>\n",
       "      <td>0.165003</td>\n",
       "      <td>0.091668</td>\n",
       "      <td>570</td>\n",
       "      <td>0.271949</td>\n",
       "      <td>0.285699</td>\n",
       "      <td>0.215420</td>\n",
       "      <td>0.872375</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2264.908332</td>\n",
       "      <td>2265.091668</td>\n",
       "      <td>0.188756</td>\n",
       "      <td>1.037378</td>\n",
       "      <td>-0.707372</td>\n",
       "      <td>570.165003</td>\n",
       "      <td>-569.834997</td>\n",
       "      <td>570.872375</td>\n",
       "      <td>-569.127625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471322</th>\n",
       "      <td>3348</td>\n",
       "      <td>0.064866</td>\n",
       "      <td>0.006986</td>\n",
       "      <td>0.216054</td>\n",
       "      <td>0.031435</td>\n",
       "      <td>1761</td>\n",
       "      <td>0.121250</td>\n",
       "      <td>0.115262</td>\n",
       "      <td>0.056883</td>\n",
       "      <td>0.957525</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3347.968565</td>\n",
       "      <td>3348.031435</td>\n",
       "      <td>0.218329</td>\n",
       "      <td>1.173579</td>\n",
       "      <td>-0.741470</td>\n",
       "      <td>1761.216054</td>\n",
       "      <td>-1760.783946</td>\n",
       "      <td>1761.957525</td>\n",
       "      <td>-1760.042475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9762</th>\n",
       "      <td>3375</td>\n",
       "      <td>0.052362</td>\n",
       "      <td>0.008813</td>\n",
       "      <td>0.333874</td>\n",
       "      <td>0.108353</td>\n",
       "      <td>1771</td>\n",
       "      <td>0.127536</td>\n",
       "      <td>0.111464</td>\n",
       "      <td>0.047696</td>\n",
       "      <td>0.918152</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3374.891647</td>\n",
       "      <td>3375.108353</td>\n",
       "      <td>0.351016</td>\n",
       "      <td>1.252026</td>\n",
       "      <td>-0.584279</td>\n",
       "      <td>1771.333874</td>\n",
       "      <td>-1770.666126</td>\n",
       "      <td>1771.918152</td>\n",
       "      <td>-1770.081848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199823</th>\n",
       "      <td>2756</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.015792</td>\n",
       "      <td>0.094751</td>\n",
       "      <td>0.017897</td>\n",
       "      <td>1142</td>\n",
       "      <td>0.086329</td>\n",
       "      <td>0.090013</td>\n",
       "      <td>0.070010</td>\n",
       "      <td>0.984884</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2755.982103</td>\n",
       "      <td>2756.017897</td>\n",
       "      <td>0.096426</td>\n",
       "      <td>1.079635</td>\n",
       "      <td>-0.890133</td>\n",
       "      <td>1142.094751</td>\n",
       "      <td>-1141.905249</td>\n",
       "      <td>1142.984884</td>\n",
       "      <td>-1141.015116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293495</th>\n",
       "      <td>3123</td>\n",
       "      <td>0.263498</td>\n",
       "      <td>0.005972</td>\n",
       "      <td>0.230654</td>\n",
       "      <td>0.020901</td>\n",
       "      <td>1445</td>\n",
       "      <td>0.154516</td>\n",
       "      <td>0.169445</td>\n",
       "      <td>0.117939</td>\n",
       "      <td>0.900222</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3122.979099</td>\n",
       "      <td>3123.020901</td>\n",
       "      <td>0.231599</td>\n",
       "      <td>1.130875</td>\n",
       "      <td>-0.669568</td>\n",
       "      <td>1445.230654</td>\n",
       "      <td>-1444.769346</td>\n",
       "      <td>1445.900222</td>\n",
       "      <td>-1444.099778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118588</th>\n",
       "      <td>2959</td>\n",
       "      <td>0.151876</td>\n",
       "      <td>0.004821</td>\n",
       "      <td>0.253127</td>\n",
       "      <td>0.018804</td>\n",
       "      <td>5325</td>\n",
       "      <td>0.093054</td>\n",
       "      <td>0.111858</td>\n",
       "      <td>0.085822</td>\n",
       "      <td>0.940185</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2958.981196</td>\n",
       "      <td>2959.018804</td>\n",
       "      <td>0.253824</td>\n",
       "      <td>1.193311</td>\n",
       "      <td>-0.687058</td>\n",
       "      <td>5325.253127</td>\n",
       "      <td>-5324.746873</td>\n",
       "      <td>5325.940185</td>\n",
       "      <td>-5324.059815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81154</th>\n",
       "      <td>2725</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.061853</td>\n",
       "      <td>0.011920</td>\n",
       "      <td>1449</td>\n",
       "      <td>0.068940</td>\n",
       "      <td>0.071840</td>\n",
       "      <td>0.047034</td>\n",
       "      <td>0.991902</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2724.988080</td>\n",
       "      <td>2725.011920</td>\n",
       "      <td>0.062991</td>\n",
       "      <td>1.053755</td>\n",
       "      <td>-0.930049</td>\n",
       "      <td>1449.061853</td>\n",
       "      <td>-1448.938147</td>\n",
       "      <td>1449.991902</td>\n",
       "      <td>-1448.008098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172300</th>\n",
       "      <td>3134</td>\n",
       "      <td>0.056193</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.026223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4014</td>\n",
       "      <td>0.137360</td>\n",
       "      <td>0.147975</td>\n",
       "      <td>0.095528</td>\n",
       "      <td>0.972761</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3134.000000</td>\n",
       "      <td>3134.000000</td>\n",
       "      <td>0.026223</td>\n",
       "      <td>0.998985</td>\n",
       "      <td>-0.946538</td>\n",
       "      <td>4014.026223</td>\n",
       "      <td>-4013.973777</td>\n",
       "      <td>4014.972761</td>\n",
       "      <td>-4013.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18948</th>\n",
       "      <td>3297</td>\n",
       "      <td>0.083211</td>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.410057</td>\n",
       "      <td>0.104951</td>\n",
       "      <td>1154</td>\n",
       "      <td>0.185163</td>\n",
       "      <td>0.164922</td>\n",
       "      <td>0.071966</td>\n",
       "      <td>0.864343</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3296.895049</td>\n",
       "      <td>3297.104951</td>\n",
       "      <td>0.423274</td>\n",
       "      <td>1.274400</td>\n",
       "      <td>-0.454286</td>\n",
       "      <td>1154.410057</td>\n",
       "      <td>-1153.589943</td>\n",
       "      <td>1154.864343</td>\n",
       "      <td>-1153.135657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210117</th>\n",
       "      <td>3027</td>\n",
       "      <td>0.058780</td>\n",
       "      <td>0.007885</td>\n",
       "      <td>0.125446</td>\n",
       "      <td>0.027957</td>\n",
       "      <td>420</td>\n",
       "      <td>0.168456</td>\n",
       "      <td>0.157704</td>\n",
       "      <td>0.082436</td>\n",
       "      <td>0.959125</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3026.972043</td>\n",
       "      <td>3027.027957</td>\n",
       "      <td>0.128524</td>\n",
       "      <td>1.084571</td>\n",
       "      <td>-0.833679</td>\n",
       "      <td>420.125446</td>\n",
       "      <td>-419.874554</td>\n",
       "      <td>420.959125</td>\n",
       "      <td>-419.040875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27403</th>\n",
       "      <td>2726</td>\n",
       "      <td>0.031864</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>0.062257</td>\n",
       "      <td>0.035786</td>\n",
       "      <td>150</td>\n",
       "      <td>0.114220</td>\n",
       "      <td>0.084317</td>\n",
       "      <td>0.025981</td>\n",
       "      <td>0.986311</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2725.964214</td>\n",
       "      <td>2726.035786</td>\n",
       "      <td>0.071809</td>\n",
       "      <td>1.048568</td>\n",
       "      <td>-0.924054</td>\n",
       "      <td>150.062257</td>\n",
       "      <td>-149.937743</td>\n",
       "      <td>150.986311</td>\n",
       "      <td>-149.013689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544627</th>\n",
       "      <td>3214</td>\n",
       "      <td>0.222704</td>\n",
       "      <td>0.002438</td>\n",
       "      <td>0.316987</td>\n",
       "      <td>0.024384</td>\n",
       "      <td>400</td>\n",
       "      <td>0.171498</td>\n",
       "      <td>0.195069</td>\n",
       "      <td>0.135736</td>\n",
       "      <td>0.873747</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3213.975616</td>\n",
       "      <td>3214.024384</td>\n",
       "      <td>0.317924</td>\n",
       "      <td>1.190734</td>\n",
       "      <td>-0.556760</td>\n",
       "      <td>400.316987</td>\n",
       "      <td>-399.683013</td>\n",
       "      <td>400.873747</td>\n",
       "      <td>-399.126253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377550</th>\n",
       "      <td>3205</td>\n",
       "      <td>0.017411</td>\n",
       "      <td>0.006727</td>\n",
       "      <td>0.243760</td>\n",
       "      <td>0.020973</td>\n",
       "      <td>4572</td>\n",
       "      <td>0.087849</td>\n",
       "      <td>0.079539</td>\n",
       "      <td>0.043133</td>\n",
       "      <td>0.961191</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3204.979027</td>\n",
       "      <td>3205.020973</td>\n",
       "      <td>0.244661</td>\n",
       "      <td>1.204951</td>\n",
       "      <td>-0.717431</td>\n",
       "      <td>4572.243760</td>\n",
       "      <td>-4571.756240</td>\n",
       "      <td>4572.961191</td>\n",
       "      <td>-4571.038809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80076</th>\n",
       "      <td>2695</td>\n",
       "      <td>0.101349</td>\n",
       "      <td>0.004235</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.003025</td>\n",
       "      <td>1538</td>\n",
       "      <td>0.056574</td>\n",
       "      <td>0.066557</td>\n",
       "      <td>0.052036</td>\n",
       "      <td>0.989586</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2694.996975</td>\n",
       "      <td>2695.003025</td>\n",
       "      <td>0.009567</td>\n",
       "      <td>0.998662</td>\n",
       "      <td>-0.980510</td>\n",
       "      <td>1538.009076</td>\n",
       "      <td>-1537.990924</td>\n",
       "      <td>1538.989586</td>\n",
       "      <td>-1537.010414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94964</th>\n",
       "      <td>2922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027974</td>\n",
       "      <td>0.003031</td>\n",
       "      <td>4147</td>\n",
       "      <td>0.050819</td>\n",
       "      <td>0.055482</td>\n",
       "      <td>0.036366</td>\n",
       "      <td>0.996105</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2921.996969</td>\n",
       "      <td>2922.003031</td>\n",
       "      <td>0.028138</td>\n",
       "      <td>1.024079</td>\n",
       "      <td>-0.968131</td>\n",
       "      <td>4147.027974</td>\n",
       "      <td>-4146.972026</td>\n",
       "      <td>4147.996105</td>\n",
       "      <td>-4146.003895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211721</th>\n",
       "      <td>3017</td>\n",
       "      <td>0.271687</td>\n",
       "      <td>0.010625</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.004553</td>\n",
       "      <td>153</td>\n",
       "      <td>0.150262</td>\n",
       "      <td>0.161646</td>\n",
       "      <td>0.116112</td>\n",
       "      <td>0.928895</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3016.995447</td>\n",
       "      <td>3017.004553</td>\n",
       "      <td>0.032197</td>\n",
       "      <td>0.960769</td>\n",
       "      <td>-0.897021</td>\n",
       "      <td>153.031874</td>\n",
       "      <td>-152.968126</td>\n",
       "      <td>153.928895</td>\n",
       "      <td>-152.071105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5455</th>\n",
       "      <td>3217</td>\n",
       "      <td>0.034133</td>\n",
       "      <td>0.002914</td>\n",
       "      <td>0.017483</td>\n",
       "      <td>-0.000416</td>\n",
       "      <td>2977</td>\n",
       "      <td>0.095738</td>\n",
       "      <td>0.094906</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>0.988599</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3217.000416</td>\n",
       "      <td>3216.999584</td>\n",
       "      <td>0.017488</td>\n",
       "      <td>1.006082</td>\n",
       "      <td>-0.971117</td>\n",
       "      <td>2977.017483</td>\n",
       "      <td>-2976.982517</td>\n",
       "      <td>2977.988599</td>\n",
       "      <td>-2976.011401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197917</th>\n",
       "      <td>2942</td>\n",
       "      <td>0.089464</td>\n",
       "      <td>0.007325</td>\n",
       "      <td>0.175267</td>\n",
       "      <td>0.029298</td>\n",
       "      <td>1866</td>\n",
       "      <td>0.119809</td>\n",
       "      <td>0.128703</td>\n",
       "      <td>0.075862</td>\n",
       "      <td>0.961089</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2941.970702</td>\n",
       "      <td>2942.029298</td>\n",
       "      <td>0.177699</td>\n",
       "      <td>1.136356</td>\n",
       "      <td>-0.785822</td>\n",
       "      <td>1866.175267</td>\n",
       "      <td>-1865.824733</td>\n",
       "      <td>1866.961089</td>\n",
       "      <td>-1865.038911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465595</th>\n",
       "      <td>2923</td>\n",
       "      <td>0.065570</td>\n",
       "      <td>0.014831</td>\n",
       "      <td>0.023418</td>\n",
       "      <td>-0.005464</td>\n",
       "      <td>323</td>\n",
       "      <td>0.189685</td>\n",
       "      <td>0.159242</td>\n",
       "      <td>0.064789</td>\n",
       "      <td>0.964037</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2923.005464</td>\n",
       "      <td>2922.994536</td>\n",
       "      <td>0.024047</td>\n",
       "      <td>0.987454</td>\n",
       "      <td>-0.940619</td>\n",
       "      <td>323.023418</td>\n",
       "      <td>-322.976582</td>\n",
       "      <td>323.964037</td>\n",
       "      <td>-322.035963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Elevation(meters)  Aspect(degrees)  Slope(degrees)  \\\n",
       "50118                2785         0.024883        0.002890   \n",
       "338704               2265         0.001528        0.036667   \n",
       "471322               3348         0.064866        0.006986   \n",
       "9762                 3375         0.052362        0.008813   \n",
       "199823               2756         0.000526        0.015792   \n",
       "293495               3123         0.263498        0.005972   \n",
       "118588               2959         0.151876        0.004821   \n",
       "81154                2725         0.005477        0.002577   \n",
       "172300               3134         0.056193        0.000624   \n",
       "18948                3297         0.083211        0.012744   \n",
       "210117               3027         0.058780        0.007885   \n",
       "27403                2726         0.031864        0.013726   \n",
       "544627               3214         0.222704        0.002438   \n",
       "377550               3205         0.017411        0.006727   \n",
       "80076                2695         0.101349        0.004235   \n",
       "94964                2922         0.000000        0.000000   \n",
       "211721               3017         0.271687        0.010625   \n",
       "5455                 3217         0.034133        0.002914   \n",
       "197917               2942         0.089464        0.007325   \n",
       "465595               2923         0.065570        0.014831   \n",
       "\n",
       "        Horizontal_Distance_To_Hydrology(meters)  \\\n",
       "50118                                   0.038850   \n",
       "338704                                  0.165003   \n",
       "471322                                  0.216054   \n",
       "9762                                    0.333874   \n",
       "199823                                  0.094751   \n",
       "293495                                  0.230654   \n",
       "118588                                  0.253127   \n",
       "81154                                   0.061853   \n",
       "172300                                  0.026223   \n",
       "18948                                   0.410057   \n",
       "210117                                  0.125446   \n",
       "27403                                   0.062257   \n",
       "544627                                  0.316987   \n",
       "377550                                  0.243760   \n",
       "80076                                   0.009076   \n",
       "94964                                   0.027974   \n",
       "211721                                  0.031874   \n",
       "5455                                    0.017483   \n",
       "197917                                  0.175267   \n",
       "465595                                  0.023418   \n",
       "\n",
       "        Vertical_Distance_To_Hydrology(meters)  \\\n",
       "50118                                 0.018943   \n",
       "338704                                0.091668   \n",
       "471322                                0.031435   \n",
       "9762                                  0.108353   \n",
       "199823                                0.017897   \n",
       "293495                                0.020901   \n",
       "118588                                0.018804   \n",
       "81154                                 0.011920   \n",
       "172300                                0.000000   \n",
       "18948                                 0.104951   \n",
       "210117                                0.027957   \n",
       "27403                                 0.035786   \n",
       "544627                                0.024384   \n",
       "377550                                0.020973   \n",
       "80076                                 0.003025   \n",
       "94964                                 0.003031   \n",
       "211721                                0.004553   \n",
       "5455                                 -0.000416   \n",
       "197917                                0.029298   \n",
       "465595                               -0.005464   \n",
       "\n",
       "        Horizontal_Distance_To_Roadways(meters)  Hillshade_9am  \\\n",
       "50118                                      3090       0.038208   \n",
       "338704                                      570       0.271949   \n",
       "471322                                     1761       0.121250   \n",
       "9762                                       1771       0.127536   \n",
       "199823                                     1142       0.086329   \n",
       "293495                                     1445       0.154516   \n",
       "118588                                     5325       0.093054   \n",
       "81154                                      1449       0.068940   \n",
       "172300                                     4014       0.137360   \n",
       "18948                                      1154       0.185163   \n",
       "210117                                      420       0.168456   \n",
       "27403                                       150       0.114220   \n",
       "544627                                      400       0.171498   \n",
       "377550                                     4572       0.087849   \n",
       "80076                                      1538       0.056574   \n",
       "94964                                      4147       0.050819   \n",
       "211721                                      153       0.150262   \n",
       "5455                                       2977       0.095738   \n",
       "197917                                     1866       0.119809   \n",
       "465595                                      323       0.189685   \n",
       "\n",
       "        Hillshade_Noon  Hillshade_3pm  \\\n",
       "50118         0.038208       0.019586   \n",
       "338704        0.285699       0.215420   \n",
       "471322        0.115262       0.056883   \n",
       "9762          0.111464       0.047696   \n",
       "199823        0.090013       0.070010   \n",
       "293495        0.169445       0.117939   \n",
       "118588        0.111858       0.085822   \n",
       "81154         0.071840       0.047034   \n",
       "172300        0.147975       0.095528   \n",
       "18948         0.164922       0.071966   \n",
       "210117        0.157704       0.082436   \n",
       "27403         0.084317       0.025981   \n",
       "544627        0.195069       0.135736   \n",
       "377550        0.079539       0.043133   \n",
       "80076         0.066557       0.052036   \n",
       "94964         0.055482       0.036366   \n",
       "211721        0.161646       0.116112   \n",
       "5455          0.094906       0.054945   \n",
       "197917        0.128703       0.075862   \n",
       "465595        0.159242       0.064789   \n",
       "\n",
       "        Horizontal_Distance_To_Fire_Points(meters)  ...  Soil_Type23_22_32_33  \\\n",
       "50118                                     0.997096  ...                     0   \n",
       "338704                                    0.872375  ...                     0   \n",
       "471322                                    0.957525  ...                     0   \n",
       "9762                                      0.918152  ...                     0   \n",
       "199823                                    0.984884  ...                     0   \n",
       "293495                                    0.900222  ...                     0   \n",
       "118588                                    0.940185  ...                     0   \n",
       "81154                                     0.991902  ...                     0   \n",
       "172300                                    0.972761  ...                     1   \n",
       "18948                                     0.864343  ...                     0   \n",
       "210117                                    0.959125  ...                     1   \n",
       "27403                                     0.986311  ...                     0   \n",
       "544627                                    0.873747  ...                     1   \n",
       "377550                                    0.961191  ...                     0   \n",
       "80076                                     0.989586  ...                     0   \n",
       "94964                                     0.996105  ...                     0   \n",
       "211721                                    0.928895  ...                     0   \n",
       "5455                                      0.988599  ...                     0   \n",
       "197917                                    0.961089  ...                     1   \n",
       "465595                                    0.964037  ...                     0   \n",
       "\n",
       "        Ele_minus_VDtHyd  Ele_plus_VDtHyd  Distanse_to_Hydrolody  \\\n",
       "50118        2784.981057      2785.018943               0.043222   \n",
       "338704       2264.908332      2265.091668               0.188756   \n",
       "471322       3347.968565      3348.031435               0.218329   \n",
       "9762         3374.891647      3375.108353               0.351016   \n",
       "199823       2755.982103      2756.017897               0.096426   \n",
       "293495       3122.979099      3123.020901               0.231599   \n",
       "118588       2958.981196      2959.018804               0.253824   \n",
       "81154        2724.988080      2725.011920               0.062991   \n",
       "172300       3134.000000      3134.000000               0.026223   \n",
       "18948        3296.895049      3297.104951               0.423274   \n",
       "210117       3026.972043      3027.027957               0.128524   \n",
       "27403        2725.964214      2726.035786               0.071809   \n",
       "544627       3213.975616      3214.024384               0.317924   \n",
       "377550       3204.979027      3205.020973               0.244661   \n",
       "80076        2694.996975      2695.003025               0.009567   \n",
       "94964        2921.996969      2922.003031               0.028138   \n",
       "211721       3016.995447      3017.004553               0.032197   \n",
       "5455         3217.000416      3216.999584               0.017488   \n",
       "197917       2941.970702      2942.029298               0.177699   \n",
       "465595       2923.005464      2922.994536               0.024047   \n",
       "\n",
       "        Hydro_plus_Fire  Hydro_minus_Fire  Hydro_plus_Road  Hydro_minus_Road  \\\n",
       "50118          1.035946         -0.958246      3090.038850      -3089.961150   \n",
       "338704         1.037378         -0.707372       570.165003       -569.834997   \n",
       "471322         1.173579         -0.741470      1761.216054      -1760.783946   \n",
       "9762           1.252026         -0.584279      1771.333874      -1770.666126   \n",
       "199823         1.079635         -0.890133      1142.094751      -1141.905249   \n",
       "293495         1.130875         -0.669568      1445.230654      -1444.769346   \n",
       "118588         1.193311         -0.687058      5325.253127      -5324.746873   \n",
       "81154          1.053755         -0.930049      1449.061853      -1448.938147   \n",
       "172300         0.998985         -0.946538      4014.026223      -4013.973777   \n",
       "18948          1.274400         -0.454286      1154.410057      -1153.589943   \n",
       "210117         1.084571         -0.833679       420.125446       -419.874554   \n",
       "27403          1.048568         -0.924054       150.062257       -149.937743   \n",
       "544627         1.190734         -0.556760       400.316987       -399.683013   \n",
       "377550         1.204951         -0.717431      4572.243760      -4571.756240   \n",
       "80076          0.998662         -0.980510      1538.009076      -1537.990924   \n",
       "94964          1.024079         -0.968131      4147.027974      -4146.972026   \n",
       "211721         0.960769         -0.897021       153.031874       -152.968126   \n",
       "5455           1.006082         -0.971117      2977.017483      -2976.982517   \n",
       "197917         1.136356         -0.785822      1866.175267      -1865.824733   \n",
       "465595         0.987454         -0.940619       323.023418       -322.976582   \n",
       "\n",
       "        Fire_plus_Road  Fire_minus_Road  \n",
       "50118      3090.997096     -3089.002904  \n",
       "338704      570.872375      -569.127625  \n",
       "471322     1761.957525     -1760.042475  \n",
       "9762       1771.918152     -1770.081848  \n",
       "199823     1142.984884     -1141.015116  \n",
       "293495     1445.900222     -1444.099778  \n",
       "118588     5325.940185     -5324.059815  \n",
       "81154      1449.991902     -1448.008098  \n",
       "172300     4014.972761     -4013.027239  \n",
       "18948      1154.864343     -1153.135657  \n",
       "210117      420.959125      -419.040875  \n",
       "27403       150.986311      -149.013689  \n",
       "544627      400.873747      -399.126253  \n",
       "377550     4572.961191     -4571.038809  \n",
       "80076      1538.989586     -1537.010414  \n",
       "94964      4147.996105     -4146.003895  \n",
       "211721      153.928895      -152.071105  \n",
       "5455       2977.988599     -2976.011401  \n",
       "197917     1866.961089     -1865.038911  \n",
       "465595      323.964037      -322.035963  \n",
       "\n",
       "[20 rows x 67 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trn[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set_rf_samples(50000)\n",
    "# reset_rf_samples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14066132249379418, 0.29434218928501793, 0.9858793969849247, 0.9166666666666666, 0.8957621440536013]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, max_features=0.7, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.     , 0.     , 0.002  , ..., 0.     , 0.     , 0.     ],\n",
       "       [0.10243, 0.     , 0.     , ..., 0.05212, 0.     , 0.84545],\n",
       "       [0.     , 0.     , 0.38578, ..., 0.05343, 0.56079, 0.     ],\n",
       "       ...,\n",
       "       [0.00417, 0.     , 0.     , ..., 0.0625 , 0.     , 0.93333],\n",
       "       [0.     , 0.07562, 0.01612, ..., 0.85446, 0.01495, 0.03885],\n",
       "       [0.9406 , 0.     , 0.     , ..., 0.     , 0.     , 0.0594 ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.predict_proba(X_train)\n",
    "# df_trn.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 5], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_trn[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 182 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.64, 0.9329523031752479)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time preds = np.stack([t.predict(X_valid) for t in m.estimators_])\n",
    "np.mean(preds[:,0]), np.std(preds[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not normally enough to just to know that a model can make accurate predictions - we also want to know *how* it's making predictions. The most important way to see this is with *feature importance*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ele_minus_VDtHyd</td>\n",
       "      <td>0.205203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ele_plus_VDtHyd</td>\n",
       "      <td>0.112898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hillshade_9am</td>\n",
       "      <td>0.051805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hillshade_Noon</td>\n",
       "      <td>0.045138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aspect(degrees)</td>\n",
       "      <td>0.044627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Hydro_plus_Fire</td>\n",
       "      <td>0.042203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vertical_Distance_To_Hydrology(meters)</td>\n",
       "      <td>0.039369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elevation(meters)</td>\n",
       "      <td>0.039153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Slope(degrees)</td>\n",
       "      <td>0.037928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hillshade_3pm</td>\n",
       "      <td>0.033737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cols       imp\n",
       "58                        Ele_minus_VDtHyd  0.205203\n",
       "59                         Ele_plus_VDtHyd  0.112898\n",
       "6                            Hillshade_9am  0.051805\n",
       "7                           Hillshade_Noon  0.045138\n",
       "1                          Aspect(degrees)  0.044627\n",
       "61                         Hydro_plus_Fire  0.042203\n",
       "4   Vertical_Distance_To_Hydrology(meters)  0.039369\n",
       "0                        Elevation(meters)  0.039153\n",
       "2                           Slope(degrees)  0.037928\n",
       "8                            Hillshade_3pm  0.033737"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = rf_feat_importance(m, df_trn); fi[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi.plot('cols', 'imp', figsize=(10,6), legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fi(fi): return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fi(fi[:30]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_all(df):\n",
    "    with pd.option_context(\"display.max_rows\", 1000, \"display.max_columns\", 1000): \n",
    "        display(df)\n",
    "# display_all(df_raw.describe(include='all').T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation(meters)', 'Aspect(degrees)', 'Slope(degrees)',\n",
       "       'Horizontal_Distance_To_Hydrology(meters)',\n",
       "       'Vertical_Distance_To_Hydrology(meters)',\n",
       "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
       "       'Hillshade_Noon', 'Hillshade_3pm',\n",
       "       'Horizontal_Distance_To_Fire_Points(meters)', 'Wilderness_Area_1',\n",
       "       'Wilderness_Area_2', 'Wilderness_Area_3', 'Wilderness_Area_4',\n",
       "       'Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
       "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
       "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
       "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
       "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
       "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
       "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
       "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
       "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
       "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_estimators':[600,700,800,1000,],'min_samples_leaf':[1], 'max_features':[0.7,1], 'n_jobs':[-1], 'oob_score':[True]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "mgrid = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_grid = GridSearchCV(mgrid,\n",
    "                        parameters,\n",
    "                        n_jobs = -1,\n",
    "                        verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    }
   ],
   "source": [
    "m_grid.fit(X_train,\n",
    "         y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2678135349345207\n",
      "{'max_features': 0.8, 'min_samples_leaf': 1, 'n_estimators': 700, 'n_jobs': -1, 'oob_score': True}\n",
      "RandomForestRegressor(max_features=0.8, n_estimators=700, n_jobs=-1,\n",
      "                      oob_score=True)\n"
     ]
    }
   ],
   "source": [
    "print(m_grid.best_score_)\n",
    "print(m_grid.best_params_)\n",
    "print(m_grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cats(df_test)\n",
    "\n",
    "dtest, ytest, nas1 = proc_df(df_test,na_dict = nas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_missing_dummy_columns( d, columns ):\n",
    "    missing_cols = set( columns ) - set( d.columns )\n",
    "    for c in missing_cols:\n",
    "        d[c] = 0\n",
    "def fix_columns( d, columns ):  \n",
    "\n",
    "    add_missing_dummy_columns( d, columns )\n",
    "\n",
    "    # make sure we have all the columns we need\n",
    "    assert( set( columns ) - set( d.columns ) == set())\n",
    "\n",
    "    extra_cols = set( d.columns ) - set( columns )\n",
    "    if extra_cols:\n",
    "        print (\"extra columns:\", extra_cols)\n",
    "\n",
    "    d = d[ columns ]\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = fix_columns(dtest,X_train.columns );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation(meters)', 'Aspect(degrees)', 'Slope(degrees)',\n",
       "       'Horizontal_Distance_To_Hydrology(meters)',\n",
       "       'Vertical_Distance_To_Hydrology(meters)',\n",
       "       'Horizontal_Distance_To_Roadways(meters)', 'Hillshade_9am',\n",
       "       'Hillshade_Noon', 'Hillshade_3pm',\n",
       "       'Horizontal_Distance_To_Fire_Points(meters)', 'Wilderness_Area_1',\n",
       "       'Wilderness_Area_2', 'Wilderness_Area_3', 'Wilderness_Area_4',\n",
       "       'Soil_Type_1', 'Soil_Type_2', 'Soil_Type_3', 'Soil_Type_4',\n",
       "       'Soil_Type_5', 'Soil_Type_6', 'Soil_Type_7', 'Soil_Type_8',\n",
       "       'Soil_Type_9', 'Soil_Type_10', 'Soil_Type_11', 'Soil_Type_12',\n",
       "       'Soil_Type_13', 'Soil_Type_14', 'Soil_Type_15', 'Soil_Type_16',\n",
       "       'Soil_Type_17', 'Soil_Type_18', 'Soil_Type_19', 'Soil_Type_20',\n",
       "       'Soil_Type_21', 'Soil_Type_22', 'Soil_Type_23', 'Soil_Type_24',\n",
       "       'Soil_Type_25', 'Soil_Type_26', 'Soil_Type_27', 'Soil_Type_28',\n",
       "       'Soil_Type_29', 'Soil_Type_30', 'Soil_Type_31', 'Soil_Type_32',\n",
       "       'Soil_Type_33', 'Soil_Type_34', 'Soil_Type_35', 'Soil_Type_36',\n",
       "       'Soil_Type_37', 'Soil_Type_38', 'Soil_Type_39', 'Soil_Type_40',\n",
       "       'binned_elevation', 'Horizontal_Distance_To_Roadways_Log',\n",
       "       'Soil_Type12_32', 'Soil_Type23_22_32_33', 'Ele_minus_VDtHyd',\n",
       "       'Ele_plus_VDtHyd', 'Distanse_to_Hydrolody', 'Hydro_plus_Fire',\n",
       "       'Hydro_minus_Fire', 'Hydro_plus_Road', 'Hydro_minus_Road',\n",
       "       'Fire_plus_Road', 'Fire_minus_Road'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.     , 0.00199, 0.     , ..., 0.84852, 0.     , 0.14949],\n",
       "       [0.95364, 0.     , 0.     , ..., 0.     , 0.     , 0.04636],\n",
       "       [0.     , 0.12732, 0.10959, ..., 0.64404, 0.05562, 0.06246],\n",
       "       ...,\n",
       "       [0.     , 0.36713, 0.     , ..., 0.39795, 0.     , 0.23493],\n",
       "       [0.     , 0.     , 0.     , ..., 0.52669, 0.     , 0.47331],\n",
       "       [0.     , 0.01541, 0.00222, ..., 0.87272, 0.00193, 0.10772]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans = m.predict_proba(dtest)\n",
    "\n",
    "ans\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ExtraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.7 s\n",
      "[6.251366444784939e-15, 0.22839674729143397, 1.0, 0.9433333333333334]\n"
     ]
    }
   ],
   "source": [
    "# Current best\n",
    "et_best = ensemble.ExtraTreesClassifier(n_estimators=300,n_jobs=-1,random_state=0)\n",
    "%time et_best.fit(X_train, y_train)\n",
    "print_score(et_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_estimators':[100,50,150,200,250,300],'min_samples_leaf':[1, 3], 'n_jobs':[-1], 'oob_score':[False]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "etgrid = ensemble.ExtraTreesClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "et_grid = GridSearchCV(etgrid,\n",
    "                        parameters,\n",
    "                        n_jobs = -1,\n",
    "                        verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  3.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=ExtraTreesClassifier(), n_jobs=-1,\n",
       "             param_grid={'min_samples_leaf': [1, 3],\n",
       "                         'n_estimators': [100, 50, 150, 200, 250, 300],\n",
       "                         'n_jobs': [-1], 'oob_score': [False]},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et_grid.fit(X_train,\n",
    "         y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(et_grid.best_score_)\n",
    "print(et_grid.best_params_)\n",
    "print(et_grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.     , 0.00333, 0.     , ..., 0.98667, 0.     , 0.01   ],\n",
       "       [1.     , 0.     , 0.     , ..., 0.     , 0.     , 0.     ],\n",
       "       [0.     , 0.00667, 0.01667, ..., 0.88333, 0.00333, 0.09   ],\n",
       "       ...,\n",
       "       [0.     , 0.12333, 0.     , ..., 0.17333, 0.     , 0.70333],\n",
       "       [0.     , 0.     , 0.     , ..., 0.28   , 0.     , 0.72   ],\n",
       "       [0.     , 0.02   , 0.     , ..., 0.60667, 0.     , 0.37333]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansreg = et_best.predict_proba(dtest)\n",
    "ansreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.     , 0.0002 , 0.     , ..., 0.98485, 0.     , 0.01495],\n",
       "       [0.91886, 0.     , 0.     , ..., 0.     , 0.     , 0.08114],\n",
       "       [0.     , 0.05773, 0.02446, ..., 0.7439 , 0.02356, 0.15025],\n",
       "       ...,\n",
       "       [0.     , 0.60821, 0.     , ..., 0.17479, 0.     , 0.21699],\n",
       "       [0.0045 , 0.     , 0.     , ..., 0.46667, 0.     , 0.52883],\n",
       "       [0.     , 0.01954, 0.00022, ..., 0.88377, 0.00019, 0.09627]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anss = ans*0.1+ansreg*0.9\n",
    "anss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 28s\n"
     ]
    }
   ],
   "source": [
    "ans_d = pd.DataFrame(ansreg, columns = ['1','2','3','4','5','6','7'])\n",
    "# dtest.drop(to_drop2, axis=1,inplace=True)\n",
    "# np.savetxt(\"final21.csv\", ans, delimiter=\",\")\n",
    "%time ans_d.to_excel(\"10.xlsx\", index = False)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra work (not in final code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation(meters)</th>\n",
       "      <th>Aspect(degrees)</th>\n",
       "      <th>Slope(degrees)</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Vertical_Distance_To_Hydrology(meters)</th>\n",
       "      <th>Horizontal_Distance_To_Roadways(meters)</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points(meters)</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>Cover_Type</th>\n",
       "      <th>binned_elevation</th>\n",
       "      <th>Horizontal_Distance_To_Roadways_Log</th>\n",
       "      <th>Soil_Type12_32</th>\n",
       "      <th>Soil_Type23_22_32_33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15220</th>\n",
       "      <td>3268</td>\n",
       "      <td>0.145295</td>\n",
       "      <td>0.009686</td>\n",
       "      <td>0.051459</td>\n",
       "      <td>0.003027</td>\n",
       "      <td>3618</td>\n",
       "      <td>0.113815</td>\n",
       "      <td>0.152560</td>\n",
       "      <td>0.120474</td>\n",
       "      <td>0.961975</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>8.193953</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12297</th>\n",
       "      <td>2684</td>\n",
       "      <td>0.020118</td>\n",
       "      <td>0.005158</td>\n",
       "      <td>0.061901</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>2122</td>\n",
       "      <td>0.114002</td>\n",
       "      <td>0.111938</td>\n",
       "      <td>0.067060</td>\n",
       "      <td>0.982684</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>7.660585</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>2795</td>\n",
       "      <td>0.025193</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.151156</td>\n",
       "      <td>0.027518</td>\n",
       "      <td>2999</td>\n",
       "      <td>0.089143</td>\n",
       "      <td>0.084492</td>\n",
       "      <td>0.046510</td>\n",
       "      <td>0.979027</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22435</th>\n",
       "      <td>3364</td>\n",
       "      <td>0.123188</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>0.611449</td>\n",
       "      <td>0.103940</td>\n",
       "      <td>3022</td>\n",
       "      <td>0.141153</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>0.103298</td>\n",
       "      <td>0.737845</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>8.014005</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26070</th>\n",
       "      <td>3206</td>\n",
       "      <td>0.022926</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.105734</td>\n",
       "      <td>0.011634</td>\n",
       "      <td>3182</td>\n",
       "      <td>0.079728</td>\n",
       "      <td>0.072200</td>\n",
       "      <td>0.036613</td>\n",
       "      <td>0.987536</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>8.065579</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15855</th>\n",
       "      <td>3352</td>\n",
       "      <td>0.263427</td>\n",
       "      <td>0.035751</td>\n",
       "      <td>0.233321</td>\n",
       "      <td>-0.013171</td>\n",
       "      <td>2440</td>\n",
       "      <td>0.459115</td>\n",
       "      <td>0.434654</td>\n",
       "      <td>0.203215</td>\n",
       "      <td>0.658566</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>7.800163</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16295</th>\n",
       "      <td>2459</td>\n",
       "      <td>0.508700</td>\n",
       "      <td>0.018240</td>\n",
       "      <td>0.192536</td>\n",
       "      <td>0.018240</td>\n",
       "      <td>895</td>\n",
       "      <td>0.407365</td>\n",
       "      <td>0.498566</td>\n",
       "      <td>0.370885</td>\n",
       "      <td>0.389125</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16196</th>\n",
       "      <td>2976</td>\n",
       "      <td>0.077197</td>\n",
       "      <td>0.012614</td>\n",
       "      <td>0.105957</td>\n",
       "      <td>0.046419</td>\n",
       "      <td>1565</td>\n",
       "      <td>0.121598</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>0.052474</td>\n",
       "      <td>0.974299</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>7.356280</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14689</th>\n",
       "      <td>3236</td>\n",
       "      <td>0.004593</td>\n",
       "      <td>0.006561</td>\n",
       "      <td>0.139101</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1355</td>\n",
       "      <td>0.136476</td>\n",
       "      <td>0.144350</td>\n",
       "      <td>0.097764</td>\n",
       "      <td>0.965177</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>7.212294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>2862</td>\n",
       "      <td>0.004616</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.058475</td>\n",
       "      <td>0.004360</td>\n",
       "      <td>2911</td>\n",
       "      <td>0.055141</td>\n",
       "      <td>0.057962</td>\n",
       "      <td>0.037957</td>\n",
       "      <td>0.994332</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>7.976595</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29050 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Elevation(meters)  Aspect(degrees)  Slope(degrees)  \\\n",
       "15220               3268         0.145295        0.009686   \n",
       "12297               2684         0.020118        0.005158   \n",
       "1606                2795         0.025193        0.004263   \n",
       "22435               3364         0.123188        0.005133   \n",
       "26070               3206         0.022926        0.004791   \n",
       "...                  ...              ...             ...   \n",
       "15855               3352         0.263427        0.035751   \n",
       "16295               2459         0.508700        0.018240   \n",
       "16196               2976         0.077197        0.012614   \n",
       "14689               3236         0.004593        0.006561   \n",
       "5018                2862         0.004616        0.001795   \n",
       "\n",
       "       Horizontal_Distance_To_Hydrology(meters)  \\\n",
       "15220                                  0.051459   \n",
       "12297                                  0.061901   \n",
       "1606                                   0.151156   \n",
       "22435                                  0.611449   \n",
       "26070                                  0.105734   \n",
       "...                                         ...   \n",
       "15855                                  0.233321   \n",
       "16295                                  0.192536   \n",
       "16196                                  0.105957   \n",
       "14689                                  0.139101   \n",
       "5018                                   0.058475   \n",
       "\n",
       "       Vertical_Distance_To_Hydrology(meters)  \\\n",
       "15220                                0.003027   \n",
       "12297                                0.006706   \n",
       "1606                                 0.027518   \n",
       "22435                                0.103940   \n",
       "26070                                0.011634   \n",
       "...                                       ...   \n",
       "15855                               -0.013171   \n",
       "16295                                0.018240   \n",
       "16196                                0.046419   \n",
       "14689                                0.000000   \n",
       "5018                                 0.004360   \n",
       "\n",
       "       Horizontal_Distance_To_Roadways(meters)  Hillshade_9am  Hillshade_Noon  \\\n",
       "15220                                     3618       0.113815        0.152560   \n",
       "12297                                     2122       0.114002        0.111938   \n",
       "1606                                      2999       0.089143        0.084492   \n",
       "22435                                     3022       0.141153        0.158476   \n",
       "26070                                     3182       0.079728        0.072200   \n",
       "...                                        ...            ...             ...   \n",
       "15855                                     2440       0.459115        0.434654   \n",
       "16295                                      895       0.407365        0.498566   \n",
       "16196                                     1565       0.121598        0.117057   \n",
       "14689                                     1355       0.136476        0.144350   \n",
       "5018                                      2911       0.055141        0.057962   \n",
       "\n",
       "       Hillshade_3pm  Horizontal_Distance_To_Fire_Points(meters)  ...  \\\n",
       "15220       0.120474                                    0.961975  ...   \n",
       "12297       0.067060                                    0.982684  ...   \n",
       "1606        0.046510                                    0.979027  ...   \n",
       "22435       0.103298                                    0.737845  ...   \n",
       "26070       0.036613                                    0.987536  ...   \n",
       "...              ...                                         ...  ...   \n",
       "15855       0.203215                                    0.658566  ...   \n",
       "16295       0.370885                                    0.389125  ...   \n",
       "16196       0.052474                                    0.974299  ...   \n",
       "14689       0.097764                                    0.965177  ...   \n",
       "5018        0.037957                                    0.994332  ...   \n",
       "\n",
       "       Soil_Type_36  Soil_Type_37  Soil_Type_38  Soil_Type_39  Soil_Type_40  \\\n",
       "15220             0             0             0             0             0   \n",
       "12297             0             0             0             0             0   \n",
       "1606              0             0             0             0             0   \n",
       "22435             0             0             0             0             0   \n",
       "26070             0             0             0             0             0   \n",
       "...             ...           ...           ...           ...           ...   \n",
       "15855             0             0             0             1             0   \n",
       "16295             0             0             0             0             0   \n",
       "16196             0             0             0             0             0   \n",
       "14689             0             0             0             0             0   \n",
       "5018              0             0             0             0             0   \n",
       "\n",
       "       Cover_Type  binned_elevation  Horizontal_Distance_To_Roadways_Log  \\\n",
       "15220           7                65                             8.193953   \n",
       "12297           3                53                             7.660585   \n",
       "1606            5                55                             8.006368   \n",
       "22435           7                67                             8.014005   \n",
       "26070           5                64                             8.065579   \n",
       "...           ...               ...                                  ...   \n",
       "15855           1                67                             7.800163   \n",
       "16295           3                49                             6.797940   \n",
       "16196           5                59                             7.356280   \n",
       "14689           7                64                             7.212294   \n",
       "5018            5                57                             7.976595   \n",
       "\n",
       "       Soil_Type12_32  Soil_Type23_22_32_33  \n",
       "15220               1                     1  \n",
       "12297               0                     0  \n",
       "1606                1                     0  \n",
       "22435               0                     1  \n",
       "26070               1                     1  \n",
       "...               ...                   ...  \n",
       "15855               0                     0  \n",
       "16295               0                     0  \n",
       "16196               1                     1  \n",
       "14689               0                     0  \n",
       "5018                0                     0  \n",
       "\n",
       "[29050 rows x 59 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2]\n",
      "[3 4 6]\n"
     ]
    }
   ],
   "source": [
    "def largest_index(inlist):\n",
    "    largest = -1\n",
    "    largest_index = 0\n",
    "    for i in range(len(inlist)):\n",
    "        item = inlist[i]\n",
    "        if item > largest:\n",
    "            largest = item\n",
    "            largest_index = i\n",
    "    return largest_index\n",
    "df_train_1_2 = df_train[(df_train['Cover_Type'] <= 2)]\n",
    "df_train_3_4_6 = df_train[(df_train['Cover_Type'].isin([3,4,6]))]\n",
    "\n",
    "X_train = df_train[feature_cols]\n",
    "X_test = df_test[feature_cols]\n",
    "\n",
    "X_train_1_2 = df_train_1_2[feature_cols]\n",
    "X_train_3_4_6 = df_train_3_4_6[feature_cols]\n",
    "\n",
    "y = df_train['Cover_Type']\n",
    "y_1_2 = df_train_1_2['Cover_Type']\n",
    "y_3_4_6 = df_train_3_4_6['Cover_Type']\n",
    "\n",
    "# test_ids = df_test['Id']\n",
    "# del df_train\n",
    "# del df_test\n",
    "\n",
    "clf = ensemble.ExtraTreesClassifier(n_estimators=100,n_jobs=-1,random_state=0)\n",
    "clf.fit(X_train, y)\n",
    "\n",
    "clf_1_2 = ensemble.RandomForestClassifier(n_estimators=200,n_jobs=-1,random_state=0)\n",
    "clf_1_2.fit(X_train_1_2, y_1_2)\n",
    "\n",
    "clf_3_4_6 = ensemble.RandomForestClassifier(n_estimators=200,n_jobs=-1,random_state=0)\n",
    "clf_3_4_6.fit(X_train_3_4_6, y_3_4_6)\n",
    "\n",
    "# del X_train\n",
    "\n",
    "vals_1_2 = {}\n",
    "for e, val in enumerate(list(clf_1_2.predict_proba(X_test))):\n",
    "    vals_1_2[e] = val\n",
    "print(clf_1_2.classes_) \n",
    "\n",
    "vals_3_4_6 = {}\n",
    "for e, val in enumerate(list(clf_3_4_6.predict_proba(X_test))):\n",
    "    vals_3_4_6[e] = val \n",
    "print(clf_3_4_6.classes_)\n",
    "\n",
    "vals = {}\n",
    "for e, val in enumerate(list(clf.predict(X_test))):\n",
    "    vals[e] = val \n",
    "\n",
    "\n",
    "# with open(loc_submission, \"w\") as outfile:\n",
    "#     outfile.write(\"Id,Cover_Type\\n\")\n",
    "ans_et = []\n",
    "for e, val in enumerate(list(clf.predict_proba(X_test))):\n",
    "    #boost types 1 and 2\n",
    "    val[0] += vals_1_2[e][0]/1.3\n",
    "    val[1] += vals_1_2[e][1]/1.1\n",
    "    #boost types 3,4, and 6\n",
    "    val[2] += vals_3_4_6[e][0]/3.4\n",
    "    val[3] += vals_3_4_6[e][1]/4.0\n",
    "    val[5] += vals_3_4_6[e][2]/3.6\n",
    "#     val = val/sum(val)\n",
    "    i = largest_index(val)\n",
    "    v = i  + 1 \n",
    "    val = [0,0,0,0,0,0,0]\n",
    "    val[v-1] = 1\n",
    "    ans_et.append(val)\n",
    "      #val[4] += vals_5_7[e][0]/2.4\n",
    "      #val[6] += vals_5_7[e][1]/3.4\n",
    "#       i = largest_index(val)\n",
    "#       v = i  + 1\n",
    "#       outfile.write(\"%s,%s\\n\"%(test_ids[e],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.0123 , 0.40076, 0.05912, 0.00057, 0.45682, 0.07043, 0.     ]),\n",
       " array([0.75488, 0.     , 0.04873, 0.00122, 0.     , 0.08798, 0.1072 ]),\n",
       " array([0.01232, 0.40154, 0.03662, 0.     , 0.39821, 0.11011, 0.04119]),\n",
       " array([0.01584, 0.46128, 0.04172, 0.     , 0.37975, 0.08769, 0.01373]),\n",
       " array([0.02396, 0.39346, 0.03502, 0.     , 0.11908, 0.09414, 0.33434]),\n",
       " array([0.02288, 0.389  , 0.06259, 0.00114, 0.20594, 0.06674, 0.2517 ]),\n",
       " array([0.0229 , 0.38932, 0.04782, 0.00115, 0.45802, 0.08079, 0.     ]),\n",
       " array([0.01759, 0.39501, 0.05919, 0.00286, 0.24241, 0.06797, 0.21497]),\n",
       " array([0.37028, 0.00442, 0.0808 , 0.00425, 0.     , 0.05402, 0.48622]),\n",
       " array([0.60436, 0.     , 0.03658, 0.00305, 0.     , 0.09754, 0.25848]),\n",
       " array([0.39184, 0.00221, 0.06726, 0.     , 0.03892, 0.07163, 0.42814]),\n",
       " array([0.02995, 0.38103, 0.07006, 0.00172, 0.43517, 0.05917, 0.0229 ]),\n",
       " array([0.12585, 0.27621, 0.01787, 0.00292, 0.34123, 0.10972, 0.12621]),\n",
       " array([0.36782, 0.00662, 0.09495, 0.     , 0.12136, 0.04517, 0.36408]),\n",
       " array([0.02642, 0.38512, 0.05994, 0.00057, 0.08702, 0.06997, 0.37096]),\n",
       " array([0.23714, 0.15091, 0.10671, 0.     , 0.01897, 0.03096, 0.45531]),\n",
       " array([0.     , 0.45599, 0.03879, 0.     , 0.37425, 0.09901, 0.03195]),\n",
       " array([0.00175, 0.41565, 0.13835, 0.     , 0.41812, 0.01704, 0.00909]),\n",
       " array([0.37411, 0.     , 0.08153, 0.00061, 0.00486, 0.05741, 0.48147]),\n",
       " array([0.82747, 0.     , 0.06087, 0.00183, 0.     , 0.07575, 0.03409]),\n",
       " array([0.37078, 0.00443, 0.05155, 0.00183, 0.     , 0.08453, 0.48689]),\n",
       " array([0.01407, 0.39919, 0.0444 , 0.00172, 0.20583, 0.08322, 0.25157]),\n",
       " array([0.37204, 0.00221, 0.08363, 0.     , 0.00486, 0.05603, 0.48122]),\n",
       " array([0.02647, 0.38584, 0.02969, 0.00229, 0.41754, 0.09687, 0.0413 ]),\n",
       " array([0.00701, 0.41514, 0.09249, 0.00456, 0.44662, 0.03418, 0.     ]),\n",
       " array([0.00878, 0.40477, 0.05641, 0.00171, 0.45209, 0.07167, 0.00457]),\n",
       " array([0.00175, 0.46214, 0.07503, 0.     , 0.40541, 0.05567, 0.     ]),\n",
       " array([0.03351, 0.37732, 0.05665, 0.     , 0.45404, 0.07389, 0.00459]),\n",
       " array([0.00878, 0.40465, 0.07049, 0.00628, 0.45197, 0.05326, 0.00457]),\n",
       " array([0.00176, 0.41294, 0.03558, 0.     , 0.38804, 0.09321, 0.06848]),\n",
       " array([0.22654, 0.16409, 0.0482 , 0.00238, 0.32775, 0.08379, 0.14725]),\n",
       " array([0.00176, 0.41338, 0.01882, 0.00114, 0.45243, 0.11247, 0.     ]),\n",
       " array([0.00176, 0.41285, 0.04161, 0.00114, 0.45642, 0.08621, 0.     ]),\n",
       " array([0.02112, 0.39113, 0.05452, 0.00114, 0.42109, 0.07438, 0.03662]),\n",
       " array([0.08728, 0.31786, 0.06061, 0.00232, 0.45848, 0.06882, 0.00463]),\n",
       " array([0.01936, 0.39313, 0.05451, 0.00229, 0.42101, 0.07309, 0.03661]),\n",
       " array([0.07107, 0.33598, 0.04756, 0.     , 0.07854, 0.08341, 0.38344]),\n",
       " array([0.36483, 0.01106, 0.04721, 0.00061, 0.42807, 0.08986, 0.05837]),\n",
       " array([0.00706, 0.4086 , 0.04546, 0.0837 , 0.     , 0.45519, 0.     ]),\n",
       " array([0.36577, 0.00882, 0.09775, 0.     , 0.0097 , 0.04246, 0.4755 ]),\n",
       " array([0.36907, 0.01104, 0.08213, 0.00304, 0.02428, 0.05396, 0.45649]),\n",
       " array([0.35064, 0.02645, 0.05919, 0.     , 0.42673, 0.0788 , 0.05819]),\n",
       " array([0.38411, 0.     , 0.06871, 0.00061, 0.37475, 0.06962, 0.1022 ]),\n",
       " array([0.34699, 0.03087, 0.0428 , 0.     , 0.16491, 0.09431, 0.32012]),\n",
       " array([0.04424, 0.36597, 0.03789, 0.00173, 0.06901, 0.0901 , 0.39107]),\n",
       " array([0.01582, 0.39689, 0.05916, 0.00114, 0.45715, 0.06984, 0.     ]),\n",
       " array([0.00175, 0.42064, 0.09635, 0.     , 0.07735, 0.03539, 0.36853]),\n",
       " array([0.0619 , 0.34948, 0.10144, 0.     , 0.44145, 0.03193, 0.0138 ]),\n",
       " array([0.18449, 0.20949, 0.09198, 0.     , 0.31979, 0.04376, 0.15049]),\n",
       " array([0.00176, 0.41317, 0.07456, 0.00171, 0.24209, 0.26671, 0.     ]),\n",
       " array([0.01055, 0.4032 , 0.03698, 0.00057, 0.45724, 0.09145, 0.     ]),\n",
       " array([0.07294, 0.33429, 0.03741, 0.00289, 0.45328, 0.08994, 0.00925]),\n",
       " array([0.39856, 0.01105, 0.06505, 0.00243, 0.00972, 0.07088, 0.44231]),\n",
       " array([0.25318, 0.13443, 0.05331, 0.0006 , 0.21942, 0.08149, 0.25758]),\n",
       " array([0.27809, 0.10665, 0.06267, 0.0006 , 0.45968, 0.07316, 0.01915]),\n",
       " array([0.36455, 0.01542, 0.10618, 0.00182, 0.04361, 0.03231, 0.43612]),\n",
       " array([0.00176, 0.41295, 0.03625, 0.00057, 0.41544, 0.09651, 0.03652]),\n",
       " array([0.08005, 0.32585, 0.06121, 0.00231, 0.06475, 0.06809, 0.39774]),\n",
       " array([0.09102, 0.31426, 0.03412, 0.00058, 0.38512, 0.09602, 0.07888]),\n",
       " array([0.00702, 0.40659, 0.0698 , 0.0057 , 0.35141, 0.05451, 0.10497]),\n",
       " array([0.41156, 0.00221, 0.05584, 0.     , 0.24829, 0.08249, 0.1996 ]),\n",
       " array([0.0035 , 0.41007, 0.35272, 0.01538, 0.0729 , 0.14542, 0.     ]),\n",
       " array([0.37776, 0.00221, 0.04299, 0.     , 0.19   , 0.09473, 0.29231]),\n",
       " array([0.0035 , 0.40983, 0.43514, 0.00569, 0.00455, 0.14129, 0.     ]),\n",
       " array([0.22828, 0.16187, 0.06005, 0.00178, 0.46532, 0.0732 , 0.0095 ]),\n",
       " array([0.35547, 0.02645, 0.06133, 0.00061, 0.0194 , 0.0761 , 0.46065]),\n",
       " array([0.42858, 0.     , 0.03943, 0.     , 0.00975, 0.09817, 0.42408]),\n",
       " array([0.03527, 0.37513, 0.06743, 0.     , 0.01375, 0.06368, 0.44474]),\n",
       " array([0.00351, 0.41471, 0.08176, 0.00456, 0.43749, 0.04431, 0.01367]),\n",
       " array([0.01936, 0.39315, 0.04913, 0.     , 0.45764, 0.08072, 0.     ]),\n",
       " array([0.06731, 0.33912, 0.09671, 0.     , 0.42369, 0.04094, 0.03224]),\n",
       " array([0.02645, 0.38547, 0.04719, 0.00229, 0.39422, 0.08022, 0.06417]),\n",
       " array([0.34301, 0.03525, 0.04704, 0.00182, 0.37806, 0.08819, 0.10663]),\n",
       " array([0.007  , 0.41428, 0.12494, 0.     , 0.41387, 0.01263, 0.02729]),\n",
       " array([0.0962 , 0.30739, 0.08651, 0.00232, 0.37982, 0.04439, 0.08337]),\n",
       " array([0.50006, 0.     , 0.09789, 0.     , 0.02429, 0.04251, 0.33524]),\n",
       " array([0.0035 , 0.40993, 0.28025, 0.00114, 0.20952, 0.09565, 0.     ]),\n",
       " array([0.34898, 0.02867, 0.04709, 0.00243, 0.48036, 0.08761, 0.00485]),\n",
       " array([0.37057, 0.00442, 0.05939, 0.00061, 0.38929, 0.0784 , 0.09732]),\n",
       " array([0.37486, 0.     , 0.04658, 0.00122, 0.00975, 0.09002, 0.47757]),\n",
       " array([0.00175, 0.41149, 0.40581, 0.00114, 0.08643, 0.09338, 0.     ]),\n",
       " array([0.3617 , 0.01322, 0.10194, 0.     , 0.04363, 0.03838, 0.44113]),\n",
       " array([0.02464, 0.38682, 0.07065, 0.     , 0.11438, 0.06037, 0.34315]),\n",
       " array([0.28593, 0.09811, 0.05572, 0.     , 0.43647, 0.0806 , 0.04317]),\n",
       " array([0.02644, 0.38533, 0.04987, 0.00057, 0.05499, 0.07955, 0.40325]),\n",
       " array([0.01934, 0.39275, 0.06858, 0.00057, 0.43888, 0.06159, 0.01829]),\n",
       " array([0.838  , 0.     , 0.04014, 0.     , 0.     , 0.09749, 0.02437]),\n",
       " array([0.09815, 0.30582, 0.05868, 0.00116, 0.42688, 0.07218, 0.03712]),\n",
       " array([0.02467, 0.38735, 0.04851, 0.00115, 0.43983, 0.08018, 0.01833]),\n",
       " array([0.23959, 0.14914, 0.06573, 0.     , 0.03804, 0.07001, 0.43748]),\n",
       " array([0.34044, 0.03738, 0.07682, 0.00121, 0.04353, 0.06046, 0.44016]),\n",
       " array([0.05491, 0.3538 , 0.05147, 0.00058, 0.44214, 0.07868, 0.01842]),\n",
       " array([0.02992, 0.3806 , 0.0868 , 0.     , 0.23335, 0.04512, 0.2242 ]),\n",
       " array([0.00527, 0.43183, 0.04836, 0.00514, 0.41564, 0.07549, 0.01827]),\n",
       " array([0.01051, 0.40156, 0.10916, 0.     , 0.     , 0.0234 , 0.45538]),\n",
       " array([0.39978, 0.00442, 0.05868, 0.00061, 0.     , 0.07908, 0.45743]),\n",
       " array([0.01581, 0.4012 , 0.0868 , 0.     , 0.42487, 0.06218, 0.00914]),\n",
       " array([0.00704, 0.40774, 0.04294, 0.004  , 0.00915, 0.52912, 0.     ]),\n",
       " array([0.00528, 0.44596, 0.02084, 0.     , 0.41604, 0.11188, 0.     ]),\n",
       " array([0.36503, 0.01106, 0.03865, 0.00122, 0.40396, 0.09734, 0.08274]),\n",
       " array([0.37248, 0.00221, 0.06226, 0.     , 0.3212 , 0.07638, 0.16546]),\n",
       " array([0.0567 , 0.35178, 0.05216, 0.     , 0.36853, 0.0787 , 0.09213]),\n",
       " array([0.01757, 0.39458, 0.07995, 0.00343, 0.45688, 0.04759, 0.     ]),\n",
       " array([0.00525, 0.43948, 0.10309, 0.00228, 0.41424, 0.02655, 0.0091 ]),\n",
       " array([0.19592, 0.19724, 0.0763 , 0.00059, 0.20281, 0.0583 , 0.26884]),\n",
       " array([0.00701, 0.40574, 0.10622, 0.00057, 0.43265, 0.03871, 0.00911]),\n",
       " array([0.02998, 0.38137, 0.05192, 0.00057, 0.44931, 0.07769, 0.00917]),\n",
       " array([0.0818 , 0.32365, 0.07003, 0.00116, 0.02774, 0.06101, 0.43461]),\n",
       " array([0.36555, 0.00882, 0.10839, 0.     , 0.04364, 0.03233, 0.44127]),\n",
       " array([0.02111, 0.39083, 0.07264, 0.00343, 0.43906, 0.05463, 0.01829]),\n",
       " array([0.34462, 0.03302, 0.0641 , 0.00061, 0.04843, 0.07332, 0.4359 ]),\n",
       " array([0.37472, 0.     , 0.0523 , 0.00061, 0.00974, 0.08525, 0.47739]),\n",
       " array([0.0982 , 0.30596, 0.06076, 0.00638, 0.02785, 0.06448, 0.43637]),\n",
       " array([0.0927 , 0.31181, 0.0593 , 0.00058, 0.10197, 0.0721 , 0.36153]),\n",
       " array([0.1201 , 0.28176, 0.04798, 0.00058, 0.24702, 0.0835 , 0.21905]),\n",
       " array([0.37063, 0.00442, 0.05511, 0.     , 0.36988, 0.08314, 0.11681]),\n",
       " array([0.3452 , 0.03308, 0.03282, 0.     , 0.35415, 0.10377, 0.13099]),\n",
       " array([0.02466, 0.38719, 0.05792, 0.00229, 0.43049, 0.06997, 0.02748]),\n",
       " array([0.25351, 0.13834, 0.09581, 0.00059, 0.0856 , 0.04095, 0.3852 ]),\n",
       " array([0.34942, 0.02636, 0.12152, 0.     , 0.29961, 0.01946, 0.18363]),\n",
       " array([0.01052, 0.42483, 0.09695, 0.     , 0.424  , 0.04369, 0.     ]),\n",
       " array([0.0035 , 0.41004, 0.07973, 0.00114, 0.41004, 0.04999, 0.04556]),\n",
       " array([0.     , 0.4155 , 0.0375 , 0.00057, 0.00914, 0.53728, 0.     ]),\n",
       " array([0.07826, 0.32792, 0.05713, 0.00231, 0.45782, 0.07194, 0.00462]),\n",
       " array([0.06741, 0.33964, 0.06444, 0.     , 0.39206, 0.06726, 0.06919]),\n",
       " array([0.01757, 0.39914, 0.07323, 0.     , 0.42946, 0.05774, 0.02284]),\n",
       " array([0.00351, 0.41027, 0.06838, 0.00057, 0.41939, 0.06141, 0.03647]),\n",
       " array([0.00175, 0.41152, 0.10745, 0.00057, 0.43675, 0.03286, 0.0091 ]),\n",
       " array([0.06571, 0.34214, 0.04007, 0.00115, 0.09697, 0.08915, 0.36481]),\n",
       " array([0.359  , 0.01768, 0.04433, 0.00304, 0.47643, 0.0898 , 0.00972]),\n",
       " array([0.20947, 0.18297, 0.04388, 0.     , 0.23205, 0.09011, 0.24152]),\n",
       " array([0.00526, 0.40798, 0.08844, 0.00285, 0.43739, 0.03987, 0.01822]),\n",
       " array([0.00352, 0.41136, 0.08348, 0.     , 0.     , 0.50164, 0.     ]),\n",
       " array([0.18047, 0.21329, 0.12007, 0.     , 0.01877, 0.01694, 0.45046]),\n",
       " array([0.     , 0.82625, 0.02418, 0.00114, 0.01827, 0.10276, 0.0274 ]),\n",
       " array([0.35083, 0.02646, 0.05066, 0.00061, 0.03396, 0.08626, 0.45122]),\n",
       " array([0.37161, 0.00221, 0.10424, 0.     , 0.00971, 0.03641, 0.47581]),\n",
       " array([0.     , 0.41952, 0.04135, 0.     , 0.01826, 0.52087, 0.     ]),\n",
       " array([0.0176 , 0.39967, 0.05315, 0.00172, 0.42545, 0.07498, 0.02745]),\n",
       " array([0.22075, 0.17033, 0.05232, 0.00059, 0.4601 , 0.08169, 0.01423]),\n",
       " array([0.00175, 0.41154, 0.31928, 0.     , 0.21384, 0.04904, 0.00455]),\n",
       " array([0.44824, 0.     , 0.03299, 0.     , 0.00975, 0.1043 , 0.40473]),\n",
       " array([0.36606, 0.00883, 0.08355, 0.     , 0.07284, 0.05598, 0.41275]),\n",
       " array([0.01053, 0.40251, 0.0678 , 0.00057, 0.4382 , 0.06213, 0.01826]),\n",
       " array([0.33785, 0.04617, 0.0505 , 0.0006 , 0.45469, 0.08599, 0.02419]),\n",
       " array([0.34661, 0.03083, 0.06341, 0.00061, 0.     , 0.07402, 0.48451]),\n",
       " array([0.35663, 0.01986, 0.06354, 0.00303, 0.21846, 0.07147, 0.26701]),\n",
       " array([0.00525, 0.45308, 0.33319, 0.     , 0.1593 , 0.04918, 0.     ]),\n",
       " array([0.37741, 0.00662, 0.10063, 0.     , 0.01456, 0.03977, 0.46102]),\n",
       " array([0.80992, 0.     , 0.01795, 0.00183, 0.00488, 0.11661, 0.04881]),\n",
       " array([0.00879, 0.40537, 0.07654, 0.     , 0.25611, 0.25319, 0.     ]),\n",
       " array([0.36576, 0.00882, 0.10417, 0.00303, 0.05337, 0.03302, 0.43182]),\n",
       " array([0.0176 , 0.39511, 0.05248, 0.00172, 0.43919, 0.07561, 0.0183 ]),\n",
       " array([0.36173, 0.01987, 0.05144, 0.00304, 0.00972, 0.08299, 0.47122]),\n",
       " array([0.37265, 0.00221, 0.05442, 0.     , 0.32621, 0.08385, 0.16067]),\n",
       " array([0.36617, 0.00883, 0.07929, 0.00061, 0.48088, 0.05937, 0.00486]),\n",
       " array([0.00526, 0.40808, 0.35184, 0.     , 0.01823, 0.21659, 0.     ]),\n",
       " array([0.0176 , 0.4227 , 0.04307, 0.     , 0.42103, 0.09102, 0.00458]),\n",
       " array([0.10908, 0.29375, 0.05812, 0.00116, 0.21387, 0.07297, 0.25106]),\n",
       " array([0.01402, 0.39779, 0.1039 , 0.     , 0.34641, 0.03305, 0.10483]),\n",
       " array([0.37226, 0.00221, 0.07653, 0.00182, 0.03405, 0.0608 , 0.45233]),\n",
       " array([0.37437, 0.     , 0.06871, 0.00061, 0.     , 0.06962, 0.48669]),\n",
       " array([0.3668 , 0.00885, 0.04723, 0.     , 0.     , 0.09056, 0.48657]),\n",
       " array([0.38343, 0.01151, 0.04581, 0.     , 0.00487, 0.09195, 0.46244]),\n",
       " array([0.01405, 0.39861, 0.07724, 0.00514, 0.45674, 0.04821, 0.     ]),\n",
       " array([0.10882, 0.29306, 0.09891, 0.00058, 0.43601, 0.03479, 0.02783]),\n",
       " array([0.23217, 0.15771, 0.05033, 0.     , 0.42777, 0.0845 , 0.04753]),\n",
       " array([0.     , 0.78524, 0.04366, 0.01199, 0.08679, 0.07232, 0.     ]),\n",
       " array([0.26096, 0.12597, 0.04708, 0.0006 , 0.18634, 0.0876 , 0.29146]),\n",
       " array([0.36617, 0.00883, 0.08286, 0.00243, 0.00971, 0.05397, 0.47602]),\n",
       " array([0.01053, 0.41139, 0.07782, 0.     , 0.40599, 0.05322, 0.04106]),\n",
       " array([0.0123 , 0.40074, 0.06113, 0.00114, 0.41569, 0.06789, 0.04111]),\n",
       " array([0.00351, 0.41073, 0.04966, 0.0057 , 0.44724, 0.08316, 0.     ]),\n",
       " array([0.17161, 0.22416, 0.0746 , 0.00059, 0.44619, 0.05936, 0.02348]),\n",
       " array([0.01055, 0.4033 , 0.03363, 0.00114, 0.45736, 0.09401, 0.     ]),\n",
       " array([0.82508, 0.00221, 0.06584, 0.00182, 0.00487, 0.07097, 0.0292 ]),\n",
       " array([0.02639, 0.3846 , 0.08407, 0.00057, 0.44364, 0.04701, 0.01372]),\n",
       " array([0.00526, 0.41279, 0.07174, 0.     , 0.16868, 0.05889, 0.28265]),\n",
       " array([0.0123 , 0.40533, 0.06248, 0.00228, 0.36546, 0.06535, 0.0868 ]),\n",
       " array([0.08559, 0.3203 , 0.03272, 0.00058, 0.30597, 0.09722, 0.15762]),\n",
       " array([0.12375, 0.27766, 0.05212, 0.00117, 0.11657, 0.07901, 0.34972]),\n",
       " array([0.0088 , 0.40549, 0.02086, 0.     , 0.45748, 0.10738, 0.     ]),\n",
       " array([0.3666 , 0.00884, 0.05721, 0.     , 0.18966, 0.08105, 0.29664]),\n",
       " array([0.20399, 0.18942, 0.02716, 0.     , 0.15153, 0.10589, 0.32201]),\n",
       " array([0.44821, 0.     , 0.04015, 0.00305, 0.04388, 0.09413, 0.37057]),\n",
       " array([0.36715, 0.01324, 0.07498, 0.00121, 0.11653, 0.06272, 0.36417]),\n",
       " array([0.02644, 0.38538, 0.05122, 0.00229, 0.23373, 0.07638, 0.22456]),\n",
       " array([0.37924, 0.     , 0.06871, 0.00061, 0.0876 , 0.06962, 0.39422]),\n",
       " array([0.35241, 0.02424, 0.06987, 0.     , 0.04363, 0.06868, 0.44117]),\n",
       " array([0.36181, 0.01988, 0.04645, 0.00243, 0.4616 , 0.08841, 0.01944]),\n",
       " array([0.007  , 0.4051 , 0.15233, 0.00057, 0.36831, 0.00758, 0.05911]),\n",
       " array([0.30242, 0.0852 , 0.05513, 0.     , 0.43736, 0.08144, 0.03845]),\n",
       " array([0.06554, 0.34124, 0.08466, 0.     , 0.05987, 0.04798, 0.4007 ]),\n",
       " array([0.0836 , 0.32163, 0.06869, 0.     , 0.37923, 0.06359, 0.08325]),\n",
       " array([0.13103, 0.2694 , 0.05902, 0.     , 0.266  , 0.07389, 0.20067]),\n",
       " array([0.0158 , 0.3962 , 0.09261, 0.00228, 0.19623, 0.03676, 0.26012]),\n",
       " array([0.37266, 0.00221, 0.05513, 0.00061, 0.     , 0.0825 , 0.48689]),\n",
       " array([0.02107, 0.39003, 0.10269, 0.     , 0.15062, 0.02979, 0.3058 ]),\n",
       " array([0.0035 , 0.43257, 0.08772, 0.     , 0.4007 , 0.04364, 0.03187]),\n",
       " array([0.00175, 0.41176, 0.42723, 0.01536, 0.     , 0.1439 , 0.     ]),\n",
       " array([0.39649, 0.00221, 0.07723, 0.     , 0.16046, 0.06213, 0.30148]),\n",
       " array([0.36866, 0.00663, 0.0551 , 0.00061, 0.36492, 0.08244, 0.12164]),\n",
       " array([0.36843, 0.00663, 0.0665 , 0.00061, 0.04862, 0.07159, 0.43762]),\n",
       " array([0.13111, 0.26957, 0.04876, 0.00058, 0.10273, 0.08302, 0.36423]),\n",
       " array([0.85791, 0.     , 0.03514, 0.00183, 0.     , 0.10024, 0.00488]),\n",
       " array([0.04783, 0.36218, 0.0298 , 0.00173, 0.12436, 0.09787, 0.33622]),\n",
       " array([0.01228, 0.4    , 0.09186, 0.     , 0.20974, 0.0399 , 0.24622]),\n",
       " array([0.     , 0.58164, 0.10031, 0.     , 0.27284, 0.03158, 0.01364]),\n",
       " array([0.10547, 0.29787, 0.06766, 0.00871, 0.32534, 0.05551, 0.13943]),\n",
       " array([0.01759, 0.39497, 0.05851, 0.00172, 0.37502, 0.06987, 0.08232]),\n",
       " array([0.37517, 0.     , 0.03658, 0.00366, 0.     , 0.09687, 0.48772]),\n",
       " array([0.36498, 0.01106, 0.04222, 0.00182, 0.48177, 0.09327, 0.00487]),\n",
       " array([0.58743, 0.01768, 0.04504, 0.00243, 0.07291, 0.08979, 0.18472]),\n",
       " array([0.24875, 0.13834, 0.09931, 0.00238, 0.05707, 0.03567, 0.41849]),\n",
       " array([0.00352, 0.4116 , 0.06187, 0.00343, 0.     , 0.51958, 0.     ]),\n",
       " array([0.01758, 0.39479, 0.06454, 0.00057, 0.45255, 0.06539, 0.00457]),\n",
       " array([0.36123, 0.01985, 0.07134, 0.00061, 0.0097 , 0.0667 , 0.47057]),\n",
       " array([0.35628, 0.01984, 0.07774, 0.00121, 0.0485 , 0.05995, 0.43648]),\n",
       " array([0.10373, 0.30015, 0.04171, 0.00349, 0.36736, 0.0859 , 0.09765]),\n",
       " array([0.02114, 0.39135, 0.0431 , 0.00057, 0.41217, 0.08587, 0.0458 ]),\n",
       " array([0.23798, 0.15144, 0.0448 , 0.00059, 0.09519, 0.08924, 0.38076]),\n",
       " array([0.34693, 0.03086, 0.04564, 0.     , 0.0291 , 0.0916 , 0.45586]),\n",
       " array([0.00176, 0.41325, 0.02687, 0.00228, 0.42944, 0.09899, 0.02741]),\n",
       " array([0.04763, 0.36068, 0.10118, 0.     , 0.38528, 0.03185, 0.07339]),\n",
       " array([0.04245, 0.3679 , 0.03787, 0.00057, 0.25293, 0.28908, 0.0092 ]),\n",
       " array([0.     , 0.41303, 0.49282, 0.00227, 0.04089, 0.05099, 0.     ]),\n",
       " array([0.36607, 0.00883, 0.08284, 0.     , 0.47589, 0.05665, 0.00971]),\n",
       " array([0.     , 0.56472, 0.06555, 0.00057, 0.19142, 0.08204, 0.09571]),\n",
       " array([0.36219, 0.01324, 0.07781, 0.     , 0.19902, 0.06135, 0.28639]),\n",
       " array([0.37036, 0.00442, 0.06937, 0.00061, 0.03891, 0.0689 , 0.44743]),\n",
       " array([0.36074, 0.01546, 0.0536 , 0.00121, 0.0729 , 0.08302, 0.41307]),\n",
       " array([0.29481, 0.0871 , 0.10709, 0.     , 0.38804, 0.03194, 0.09102]),\n",
       " array([0.01052, 0.40191, 0.09585, 0.00114, 0.45121, 0.03482, 0.00456]),\n",
       " array([0.     , 0.41326, 0.16606, 0.00057, 0.37731, 0.02462, 0.01818]),\n",
       " array([0.02644, 0.39916, 0.04651, 0.00057, 0.0825 , 0.08275, 0.36207]),\n",
       " array([0.35533, 0.0221 , 0.02503, 0.00122, 0.08266, 0.11008, 0.40358]),\n",
       " array([0.36881, 0.00664, 0.04796, 0.00061, 0.05841, 0.08924, 0.42834]),\n",
       " array([0.35982, 0.01542, 0.10051, 0.00182, 0.00969, 0.0377 , 0.47503]),\n",
       " array([0.00176, 0.41312, 0.02888, 0.00057, 0.42475, 0.13093, 0.     ]),\n",
       " array([0.3658 , 0.00882, 0.09633, 0.     , 0.12131, 0.04381, 0.36393]),\n",
       " array([0.35705, 0.01988, 0.04146, 0.00243, 0.41313, 0.09316, 0.0729 ]),\n",
       " array([0.     , 0.41536, 0.05133, 0.00457, 0.27871, 0.25003, 0.     ]),\n",
       " array([0.02458, 0.3859 , 0.11344, 0.     , 0.1415 , 0.01965, 0.31494]),\n",
       " array([0.3752 , 0.00442, 0.06937, 0.     , 0.37932, 0.06957, 0.10212]),\n",
       " array([0.00701, 0.4062 , 0.47793, 0.01425, 0.     , 0.09461, 0.     ]),\n",
       " array([0.80315, 0.     , 0.05658, 0.     , 0.     , 0.08184, 0.05844]),\n",
       " array([0.12006, 0.28167, 0.05276, 0.     , 0.32614, 0.07959, 0.13978]),\n",
       " array([0.00175, 0.41127, 0.19016, 0.     , 0.34555, 0.04673, 0.00455]),\n",
       " array([0.00528, 0.42327, 0.02287, 0.00457, 0.44364, 0.10037, 0.     ]),\n",
       " array([0.00175, 0.41181, 0.09038, 0.00285, 0.44161, 0.03794, 0.01366]),\n",
       " array([0.1455 , 0.25263, 0.09135, 0.     , 0.01868, 0.04346, 0.44837]),\n",
       " array([0.00527, 0.45013, 0.05307, 0.00799, 0.1553 , 0.06788, 0.26036]),\n",
       " array([0.     , 0.47833, 0.05699, 0.00228, 0.33284, 0.07029, 0.05927]),\n",
       " array([0.00175, 0.416  , 0.10034, 0.     , 0.41848, 0.03159, 0.03184]),\n",
       " array([0.0693 , 0.33809, 0.04348, 0.00115, 0.04158, 0.08598, 0.42041]),\n",
       " array([0.08401, 0.34161, 0.02926, 0.00058, 0.40724, 0.10027, 0.03702]),\n",
       " array([0.02469, 0.3877 , 0.03574, 0.00287, 0.44481, 0.09044, 0.01376]),\n",
       " array([0.01228, 0.40022, 0.08655, 0.00228, 0.41059, 0.04245, 0.04562]),\n",
       " array([0.1107 , 0.29121, 0.08807, 0.     , 0.1207 , 0.04578, 0.34354]),\n",
       " array([0.00175, 0.41195, 0.19248, 0.00455, 0.28692, 0.10234, 0.     ]),\n",
       " array([0.37042, 0.00442, 0.06795, 0.00122, 0.00973, 0.06958, 0.47668]),\n",
       " array([0.37727, 0.00221, 0.06583, 0.     , 0.01946, 0.07298, 0.46224]),\n",
       " array([0.39479, 0.00442, 0.06782, 0.     , 0.01459, 0.07567, 0.44269]),\n",
       " array([0.09093, 0.31396, 0.05113, 0.00058, 0.35695, 0.07984, 0.10662]),\n",
       " array([0.3153 , 0.06576, 0.06524, 0.00784, 0.04822, 0.06363, 0.43401]),\n",
       " array([0.01755, 0.39408, 0.09797, 0.00114, 0.0867 , 0.03296, 0.36961]),\n",
       " array([0.0353 , 0.39838, 0.05533, 0.00115, 0.33497, 0.07393, 0.10095]),\n",
       " array([0.35639, 0.01985, 0.07919, 0.00485, 0.04366, 0.05458, 0.44148]),\n",
       " array([0.05308, 0.35546, 0.07306, 0.00345, 0.41861, 0.05495, 0.0414 ]),\n",
       " array([0.0035 , 0.46463, 0.08039, 0.     , 0.39175, 0.05061, 0.00911]),\n",
       " array([0.00175, 0.41116, 0.11751, 0.     , 0.43637, 0.01957, 0.01364]),\n",
       " array([0.78565, 0.00221, 0.07367, 0.     , 0.     , 0.06552, 0.07295]),\n",
       " array([0.25693, 0.13013, 0.06526, 0.00358, 0.26244, 0.06694, 0.21472]),\n",
       " array([0.00352, 0.4115 , 0.02286, 0.00457, 0.45722, 0.10033, 0.     ]),\n",
       " array([0.36944, 0.01105, 0.05933, 0.00061, 0.03403, 0.07832, 0.44722]),\n",
       " array([0.00175, 0.51202, 0.08303, 0.     , 0.26408, 0.04806, 0.09106]),\n",
       " array([0.03888, 0.37175, 0.04797, 0.00345, 0.43649, 0.07849, 0.02297]),\n",
       " array([0.3792 , 0.     , 0.06942, 0.     , 0.0292 , 0.06962, 0.45257]),\n",
       " array([0.02998, 0.38595, 0.05461, 0.00172, 0.44471, 0.07386, 0.00917]),\n",
       " array([0.0035 , 0.40921, 0.15593, 0.00057, 0.33191, 0.0216 , 0.07729]),\n",
       " array([0.03532, 0.3757 , 0.04119, 0.00057, 0.3444 , 0.08801, 0.1148 ]),\n",
       " array([0.00878, 0.40485, 0.05508, 0.00285, 0.42935, 0.07168, 0.02741]),\n",
       " array([0.     , 0.45895, 0.11063, 0.     , 0.38659, 0.03474, 0.0091 ]),\n",
       " array([0.36389, 0.01103, 0.09347, 0.00061, 0.05337, 0.04582, 0.43181]),\n",
       " array([0.36044, 0.01545, 0.06712, 0.00061, 0.     , 0.07081, 0.48557]),\n",
       " array([0.11453, 0.28762, 0.06021, 0.     , 0.41409, 0.07237, 0.05118]),\n",
       " array([0.2259 , 0.16363, 0.09682, 0.00118, 0.22736, 0.03881, 0.2463 ]),\n",
       " array([0.34416, 0.03298, 0.09176, 0.00242, 0.0532 , 0.04501, 0.43048]),\n",
       " array([0.22676, 0.16425, 0.02937, 0.00178, 0.07132, 0.10236, 0.40415]),\n",
       " array([0.21286, 0.17846, 0.07652, 0.     , 0.17502, 0.05913, 0.29801]),\n",
       " array([0.00176, 0.46315, 0.03692, 0.00057, 0.35608, 0.0913 , 0.05022]),\n",
       " array([0.64162, 0.00222, 0.02224, 0.00061, 0.     , 0.11382, 0.2195 ]),\n",
       " array([0.37049, 0.00442, 0.06224, 0.     , 0.01946, 0.07635, 0.46704]),\n",
       " array([0.2606 , 0.1258 , 0.07368, 0.00179, 0.44853, 0.06097, 0.02863]),\n",
       " array([0.00351, 0.41038, 0.14444, 0.     , 0.32831, 0.10424, 0.00912]),\n",
       " array([0.24561, 0.14297, 0.04625, 0.0006 , 0.45272, 0.08803, 0.02383]),\n",
       " array([0.01931, 0.39211, 0.098  , 0.00057, 0.44273, 0.0336 , 0.01369]),\n",
       " array([0.33531, 0.04403, 0.03348, 0.00061, 0.47949, 0.10225, 0.00484]),\n",
       " array([0.00176, 0.41331, 0.12552, 0.02342, 0.11423, 0.32176, 0.     ]),\n",
       " array([0.     , 0.41553, 0.04396, 0.     , 0.01828, 0.52222, 0.     ]),\n",
       " array([0.01931, 0.39215, 0.09733, 0.00114, 0.4519 , 0.0336 , 0.00456]),\n",
       " array([0.00526, 0.41311, 0.06441, 0.00342, 0.45169, 0.0621 , 0.     ]),\n",
       " array([0.37568, 0.00883, 0.08781, 0.     , 0.     , 0.05192, 0.47576]),\n",
       " array([0.36057, 0.01546, 0.06215, 0.00121, 0.00486, 0.07488, 0.48088]),\n",
       " array([0.41868, 0.     , 0.04586, 0.     , 0.01462, 0.09204, 0.4288 ]),\n",
       " array([0.35462, 0.02206, 0.05852, 0.     , 0.28146, 0.07953, 0.20381]),\n",
       " array([0.37054, 0.00442, 0.05939, 0.     , 0.39899, 0.07907, 0.08758]),\n",
       " array([0.3586 , 0.01766, 0.05856, 0.     , 0.00486, 0.07958, 0.48075]),\n",
       " array([0.01581, 0.39641, 0.07856, 0.     , 0.43833, 0.05264, 0.01826]),\n",
       " array([0.2171 , 0.17464, 0.03906, 0.     , 0.07115, 0.09487, 0.40318]),\n",
       " array([0.0176 , 0.39516, 0.04643, 0.     , 0.44841, 0.08325, 0.00915]),\n",
       " array([0.00527, 0.40908, 0.04017, 0.00057, 0.42486, 0.11548, 0.00457]),\n",
       " array([0.36868, 0.00664, 0.05438, 0.00061, 0.3698 , 0.08312, 0.11678]),\n",
       " array([0.00702, 0.44735, 0.08585, 0.00741, 0.41045, 0.04193, 0.     ]),\n",
       " array([0.36277, 0.01326, 0.04933, 0.     , 0.00972, 0.08846, 0.47646]),\n",
       " array([0.00176, 0.41282, 0.04295, 0.00114, 0.45639, 0.08494, 0.     ]),\n",
       " array([0.11103, 0.29208, 0.0404 , 0.00291, 0.42372, 0.08795, 0.04191]),\n",
       " array([0.35991, 0.01543, 0.09341, 0.00061, 0.14061, 0.04579, 0.34425]),\n",
       " array([0.18289, 0.21186, 0.072  , 0.00294, 0.18832, 0.0595 , 0.28248]),\n",
       " array([0.36017, 0.01544, 0.0842 , 0.00243, 0.37846, 0.05256, 0.10674]),\n",
       " array([0.05863, 0.35063, 0.00951, 0.     , 0.45267, 0.12395, 0.00462]),\n",
       " array([0.01583, 0.39693, 0.05715, 0.00114, 0.36119, 0.07175, 0.09601]),\n",
       " array([0.08916, 0.3161 , 0.05727, 0.00753, 0.31989, 0.06632, 0.14372]),\n",
       " array([0.37246, 0.00221, 0.06441, 0.00061, 0.33091, 0.07367, 0.15572]),\n",
       " array([0.03352, 0.40033, 0.05329, 0.     , 0.27522, 0.07709, 0.16055]),\n",
       " array([0.06391, 0.34409, 0.04073, 0.00058, 0.45235, 0.08911, 0.00923]),\n",
       " array([0.00701, 0.4471 , 0.08111, 0.     , 0.41478, 0.05001, 0.     ]),\n",
       " array([0.08914, 0.31604, 0.04567, 0.     , 0.44035, 0.08562, 0.02318]),\n",
       " array([0.     , 0.41542, 0.01344, 0.00057, 0.02285, 0.54772, 0.     ]),\n",
       " array([0.     , 0.41552, 0.04006, 0.     , 0.     , 0.54442, 0.     ]),\n",
       " array([0.12913, 0.2713 , 0.0672 , 0.     , 0.31242, 0.06606, 0.15388]),\n",
       " array([0.1266 , 0.27974, 0.04731, 0.00058, 0.45692, 0.08418, 0.00466]),\n",
       " array([0.35427, 0.02204, 0.07628, 0.     , 0.     , 0.06262, 0.48479]),\n",
       " array([0.00703, 0.40715, 0.08387, 0.     , 0.08226, 0.41968, 0.     ]),\n",
       " array([0.12907, 0.27117, 0.07608, 0.     , 0.42879, 0.05761, 0.03729]),\n",
       " array([0.0035 , 0.40921, 0.29099, 0.     , 0.15459, 0.05077, 0.09094]),\n",
       " array([0.08518, 0.31877, 0.11738, 0.     , 0.39678, 0.0173 , 0.06459]),\n",
       " array([0.81395, 0.     , 0.03299, 0.     , 0.     , 0.1043 , 0.04876]),\n",
       " array([0.00879, 0.80289, 0.03429, 0.     , 0.05943, 0.0946 , 0.     ]),\n",
       " array([0.     , 0.41319, 0.49528, 0.     , 0.01818, 0.07335, 0.     ]),\n",
       " array([0.00175, 0.41671, 0.07558, 0.00057, 0.42831, 0.05885, 0.01823]),\n",
       " array([0.01761, 0.39532, 0.0451 , 0.00286, 0.43943, 0.08138, 0.01831]),\n",
       " array([0.36027, 0.01544, 0.07566, 0.00061, 0.41739, 0.06269, 0.06795]),\n",
       " array([0.12187, 0.27958, 0.05687, 0.     , 0.03262, 0.07572, 0.43334]),\n",
       " array([0.     , 0.41401, 0.088  , 0.00057, 0.43264, 0.05566, 0.00911]),\n",
       " array([0.38421, 0.     , 0.06372, 0.00061, 0.00487, 0.07438, 0.47222]),\n",
       " array([0.36214, 0.01324, 0.08636, 0.00303, 0.1359 , 0.04988, 0.34945]),\n",
       " array([0.37039, 0.00442, 0.06938, 0.00122, 0.00486, 0.06823, 0.4815 ]),\n",
       " array([0.00702, 0.40663, 0.06242, 0.00285, 0.38796, 0.06466, 0.06846]),\n",
       " array([0.06207, 0.3458 , 0.05967, 0.00288, 0.0876 , 0.06852, 0.37346]),\n",
       " array([0.36878, 0.00664, 0.04939, 0.00061, 0.05841, 0.08788, 0.42831]),\n",
       " array([0.35426, 0.02204, 0.07699, 0.     , 0.08241, 0.06194, 0.40236]),\n",
       " array([0.01408, 0.39936, 0.03567, 0.00114, 0.39811, 0.09216, 0.05949]),\n",
       " array([0.78194, 0.00221, 0.04942, 0.00122, 0.     , 0.08727, 0.07793]),\n",
       " array([0.35205, 0.02422, 0.08831, 0.     , 0.37775, 0.05112, 0.10655]),\n",
       " array([0.36617, 0.00883, 0.07929, 0.00061, 0.08743, 0.05937, 0.3983 ]),\n",
       " array([0.35462, 0.02206, 0.05994, 0.00061, 0.04367, 0.07751, 0.44159]),\n",
       " array([0.00878, 0.40475, 0.06178, 0.004  , 0.45207, 0.06406, 0.00457]),\n",
       " array([0.0105 , 0.40592, 0.1178 , 0.     , 0.4324 , 0.01517, 0.01821]),\n",
       " array([0.00525, 0.40733, 0.12924, 0.     , 0.4003 , 0.02148, 0.03639]),\n",
       " array([0.00701, 0.43351, 0.07978, 0.00114, 0.4103 , 0.05002, 0.01824]),\n",
       " array([0.35237, 0.02424, 0.072  , 0.     , 0.01939, 0.06665, 0.46535]),\n",
       " array([0.34695, 0.03086, 0.06776, 0.01152, 0.37829, 0.05793, 0.1067 ]),\n",
       " array([0.00176, 0.80614, 0.02351, 0.00057, 0.05025, 0.10406, 0.01371]),\n",
       " array([0.21495, 0.17653, 0.06687, 0.00355, 0.     , 0.06447, 0.47363]),\n",
       " array([0.37473, 0.01544, 0.0792 , 0.     , 0.37361, 0.05998, 0.09704]),\n",
       " array([0.04252, 0.36854, 0.02845, 0.     , 0.01843, 0.54206, 0.     ]),\n",
       " array([0.8388 , 0.     , 0.02296, 0.     , 0.     , 0.11384, 0.02439]),\n",
       " array([0.00702, 0.41573, 0.05772, 0.     , 0.3925 , 0.07226, 0.05477]),\n",
       " array([0.36816, 0.00663, 0.08432, 0.00304, 0.     , 0.05196, 0.4859 ]),\n",
       " array([0.0123 , 0.40081, 0.08318, 0.     , 0.37921, 0.1245 , 0.     ]),\n",
       " array([0.32318, 0.05707, 0.05184, 0.00181, 0.46843, 0.08317, 0.01449]),\n",
       " array([0.35861, 0.01766, 0.05785, 0.     , 0.01457, 0.08026, 0.47105]),\n",
       " array([0.10902, 0.29358, 0.06628, 0.     , 0.06041, 0.06647, 0.40425]),\n",
       " array([0.00526, 0.41306, 0.05971, 0.     , 0.43339, 0.07489, 0.01369]),\n",
       " array([0.00527, 0.418  , 0.0564 , 0.00628, 0.44747, 0.06659, 0.     ]),\n",
       " array([0.86133, 0.     , 0.063  , 0.00061, 0.     , 0.07505, 0.     ]),\n",
       " array([0.37502, 0.00442, 0.08435, 0.00304, 0.07291, 0.05198, 0.40829]),\n",
       " array([0.05854, 0.35009, 0.0373 , 0.     , 0.44736, 0.09288, 0.01384]),\n",
       " array([0.23525, 0.15302, 0.10598, 0.     , 0.20388, 0.03161, 0.27026]),\n",
       " array([0.00352, 0.41158, 0.01143, 0.00057, 0.45731, 0.1156 , 0.     ]),\n",
       " array([0.     , 0.41925, 0.01004, 0.4485 , 0.     , 0.12221, 0.     ]),\n",
       " array([0.02821, 0.38343, 0.04854, 0.00115, 0.43552, 0.08023, 0.02292]),\n",
       " array([0.00704, 0.40756, 0.05086, 0.00629, 0.03202, 0.49623, 0.     ]),\n",
       " array([0.00701, 0.40577, 0.09578, 0.00057, 0.1503 , 0.03542, 0.30515]),\n",
       " array([0.37107, 0.00443, 0.03654, 0.00122, 0.47751, 0.09948, 0.00975]),\n",
       " array([0.00175, 0.46243, 0.06368, 0.     , 0.40111, 0.06647, 0.00456]),\n",
       " array([0.04594, 0.36796, 0.06554, 0.     , 0.40889, 0.06572, 0.04594]),\n",
       " array([0.0123 , 0.40076, 0.05778, 0.     , 0.45683, 0.07233, 0.     ]),\n",
       " array([0.02291, 0.38947, 0.03976, 0.00057, 0.43529, 0.08909, 0.02291]),\n",
       " array([0.00175, 0.41245, 0.07846, 0.01083, 0.39214, 0.04509, 0.05928]),\n",
       " array([0.00175, 0.4161 , 0.203  , 0.00114, 0.28664, 0.08683, 0.00455]),\n",
       " array([0.00878, 0.40459, 0.0631 , 0.00114, 0.41538, 0.06593, 0.04108]),\n",
       " array([0.     , 0.42237, 0.14588, 0.     , 0.39096, 0.02715, 0.01364]),\n",
       " array([0.24753, 0.14085, 0.04837, 0.00179, 0.03337, 0.08475, 0.44335]),\n",
       " array([0.40422, 0.     , 0.03871, 0.     , 0.     , 0.09885, 0.45822]),\n",
       " array([0.0035 , 0.40945, 0.49897, 0.00227, 0.01365, 0.07216, 0.     ]),\n",
       " array([0.3677 , 0.00662, 0.10063, 0.     , 0.02912, 0.03977, 0.45617]),\n",
       " array([0.00704, 0.41207, 0.03646, 0.     , 0.04574, 0.49869, 0.     ]),\n",
       " array([0.02639, 0.39843, 0.07804, 0.     , 0.43004, 0.05337, 0.01372]),\n",
       " array([0.36678, 0.00885, 0.05295, 0.00243, 0.02919, 0.08244, 0.45736]),\n",
       " array([0.00527, 0.42236, 0.18926, 0.     , 0.     , 0.38311, 0.     ]),\n",
       " array([0.02115, 0.39156, 0.03841, 0.00286, 0.45362, 0.08782, 0.00458]),\n",
       " array([0.34886, 0.02866, 0.04993, 0.00061, 0.22797, 0.0869 , 0.25707]),\n",
       " array([0.00527, 0.41385, 0.02554, 0.     , 0.45249, 0.10284, 0.     ]),\n",
       " array([0.12549, 0.27543, 0.06306, 0.     , 0.34958, 0.06992, 0.11653]),\n",
       " array([0.02292, 0.38971, 0.03304, 0.00287, 0.44473, 0.09297, 0.01375]),\n",
       " array([0.01408, 0.40855, 0.035  , 0.00172, 0.43476, 0.09216, 0.01373]),\n",
       " array([0.19206, 0.20129, 0.08452, 0.     , 0.10835, 0.05104, 0.36274]),\n",
       " array([0.08372, 0.32207, 0.04563, 0.00058, 0.22692, 0.0849 , 0.23618]),\n",
       " array([0.01938, 0.39358, 0.03436, 0.00229, 0.43522, 0.09226, 0.02291]),\n",
       " array([0.00175, 0.41205, 0.46491, 0.00171, 0.03189, 0.08769, 0.     ]),\n",
       " array([0.06748, 0.34   , 0.04549, 0.     , 0.43403, 0.08529, 0.0277 ]),\n",
       " array([0.38284, 0.00663, 0.08291, 0.00486, 0.00486, 0.0513 , 0.46659]),\n",
       " array([0.00176, 0.4132 , 0.13382, 0.     , 0.     , 0.45122, 0.     ]),\n",
       " array([0.03109, 0.39545, 0.0054 , 0.     , 0.01378, 0.55427, 0.     ]),\n",
       " array([0.07449, 0.33115, 0.099  , 0.00288, 0.38732, 0.03138, 0.07378]),\n",
       " array([0.01576, 0.39534, 0.32238, 0.     , 0.25045, 0.00696, 0.00911]),\n",
       " array([0.37253, 0.00221, 0.06012, 0.     , 0.41858, 0.07842, 0.06814]),\n",
       " array([0.     , 0.42688, 0.13945, 0.00057, 0.33184, 0.02399, 0.07728]),\n",
       " array([0.01406, 0.3988 , 0.05981, 0.00057, 0.45238, 0.06981, 0.00457]),\n",
       " array([0.     , 0.4149 , 0.03946, 0.     , 0.43813, 0.10294, 0.00456]),\n",
       " array([0.03174, 0.37928, 0.05798, 0.00057, 0.32551, 0.07195, 0.13296]),\n",
       " array([0.08363, 0.32173, 0.06327, 0.     , 0.16192, 0.06875, 0.3007 ]),\n",
       " array([0.36217, 0.01324, 0.07995, 0.00061, 0.     , 0.05865, 0.48539]),\n",
       " array([0.36218, 0.01324, 0.07852, 0.     , 0.46598, 0.06067, 0.01942]),\n",
       " array([0.85569, 0.     , 0.07797, 0.     , 0.     , 0.06148, 0.00486]),\n",
       " array([0.22823, 0.16184, 0.06004, 0.     , 0.10444, 0.07516, 0.37029]),\n",
       " array([0.00175, 0.41712, 0.066  , 0.     , 0.39224, 0.11833, 0.00456]),\n",
       " array([0.00351, 0.76149, 0.06236, 0.     , 0.05472, 0.06776, 0.05016]),\n",
       " array([0.25315, 0.13441, 0.05401, 0.     , 0.15262, 0.08148, 0.32433]),\n",
       " array([0.03353, 0.37744, 0.05195, 0.00057, 0.44042, 0.07774, 0.01835]),\n",
       " array([0.34436, 0.033  , 0.07615, 0.     , 0.06776, 0.06251, 0.41621]),\n",
       " array([0.00525, 0.41232, 0.09764, 0.00057, 0.44171, 0.03795, 0.00455]),\n",
       " array([0.28399, 0.10025, 0.0557 , 0.     , 0.47468, 0.08058, 0.00479]),\n",
       " array([0.33522, 0.04402, 0.04059, 0.00182, 0.46968, 0.09415, 0.01453]),\n",
       " array([0.09279, 0.3121 , 0.04366, 0.00116, 0.06031, 0.08634, 0.40363]),\n",
       " array([0.3617 , 0.01322, 0.10194, 0.     , 0.01454, 0.03838, 0.47021]),\n",
       " array([0.0035 , 0.4098 , 0.09227, 0.     , 0.41891, 0.04364, 0.03187]),\n",
       " array([0.40684, 0.00221, 0.04942, 0.     , 0.00487, 0.08861, 0.44805]),\n",
       " array([0.24357, 0.14501, 0.05532, 0.0006 , 0.10475, 0.07936, 0.3714 ]),\n",
       " array([0.01934, 0.39278, 0.06724, 0.00057, 0.27889, 0.06287, 0.17831]),\n",
       " array([0.00877, 0.40416, 0.0798 , 0.     , 0.43774, 0.0513 , 0.01824]),\n",
       " array([0.36144, 0.01549, 0.02005, 0.00183, 0.0633 , 0.11429, 0.42361]),\n",
       " array([0.36815, 0.00663, 0.0786 , 0.     , 0.13605, 0.06074, 0.34984]),\n",
       " array([0.     , 0.49071, 0.09964, 0.     , 0.31377, 0.03221, 0.06366]),\n",
       " array([0.22953, 0.1663 , 0.03494, 0.00119, 0.27083, 0.09767, 0.19956]),\n",
       " array([0.01406, 0.40333, 0.06585, 0.00286, 0.41122, 0.06156, 0.04112]),\n",
       " array([0.01056, 0.40337, 0.0296 , 0.00057, 0.45286, 0.09847, 0.00457]),\n",
       " array([0.00176, 0.41335, 0.03253, 0.00514, 0.     , 0.54722, 0.     ]),\n",
       " array([0.01052, 0.60721, 0.08984, 0.00114, 0.2462 , 0.04053, 0.00456]),\n",
       " array([0.01582, 0.39689, 0.06118, 0.00229, 0.42058, 0.06667, 0.03657]),\n",
       " array([0.36291, 0.01326, 0.0422 , 0.     , 0.39396, 0.09525, 0.09241]),\n",
       " array([0.09612, 0.30713, 0.10277, 0.00289, 0.06016, 0.02828, 0.40264]),\n",
       " array([0.00527, 0.4132 , 0.06174, 0.00399, 0.31944, 0.06402, 0.13234]),\n",
       " array([0.01936, 0.39313, 0.05115, 0.00057, 0.45761, 0.07818, 0.     ]),\n",
       " array([0.36777, 0.00662, 0.09708, 0.     , 0.37375, 0.04315, 0.11164]),\n",
       " array([0.00878, 0.42277, 0.06644, 0.00114, 0.4153 , 0.06275, 0.02282]),\n",
       " array([0.02821, 0.38339, 0.04921, 0.00057, 0.4584 , 0.08022, 0.     ]),\n",
       " array([0.09458, 0.31001, 0.0464 , 0.     , 0.0464 , 0.08506, 0.41756]),\n",
       " array([0.38422, 0.     , 0.06587, 0.00183, 0.00487, 0.071  , 0.47222]),\n",
       " array([0.0088 , 0.40576, 0.02841, 0.00515, 0.     , 0.55188, 0.     ]),\n",
       " array([0.24729, 0.14071, 0.06443, 0.00119, 0.24765, 0.07012, 0.2286 ]),\n",
       " array([0.01407, 0.39906, 0.04774, 0.00057, 0.3841 , 0.08129, 0.07316]),\n",
       " array([0.00176, 0.41373, 0.03565, 0.04231, 0.     , 0.50656, 0.     ]),\n",
       " array([0.34896, 0.02867, 0.04923, 0.00303, 0.45123, 0.08491, 0.03396]),\n",
       " array([0.09281, 0.31219, 0.0389 , 0.00116, 0.46406, 0.09088, 0.     ]),\n",
       " array([0.36488, 0.01106, 0.04364, 0.     , 0.01946, 0.09392, 0.46704]),\n",
       " array([0.31595, 0.08089, 0.05729, 0.0012 , 0.06733, 0.07815, 0.39919]),\n",
       " array([0.00175, 0.6079 , 0.07503, 0.     , 0.25509, 0.05567, 0.00456]),\n",
       " array([0.38199, 0.00221, 0.07296, 0.     , 0.05836, 0.0662 , 0.41828]),\n",
       " array([0.36878, 0.00664, 0.04796, 0.     , 0.42344, 0.08991, 0.06327]),\n",
       " array([0.00176, 0.45382, 0.04496, 0.00057, 0.36048, 0.08366, 0.05476]),\n",
       " array([0.04714, 0.36862, 0.00596, 0.     , 0.03686, 0.54141, 0.     ]),\n",
       " array([0.21106, 0.18062, 0.08835, 0.00946, 0.12773, 0.03745, 0.34533]),\n",
       " array([0.04063, 0.37408, 0.05538, 0.     , 0.40414, 0.07527, 0.05052]),\n",
       " array([0.01052, 0.40195, 0.09385, 0.00114, 0.45582, 0.03672, 0.     ]),\n",
       " array([0.02642, 0.38509, 0.0633 , 0.00172, 0.41673, 0.06551, 0.04122]),\n",
       " array([0.09625, 0.31218, 0.07292, 0.     , 0.35684, 0.05986, 0.10195]),\n",
       " array([0.00175, 0.6584 , 0.06435, 0.     , 0.13674, 0.06584, 0.07293]),\n",
       " array([0.35404, 0.02202, 0.09262, 0.00242, 0.07752, 0.04441, 0.40696]),\n",
       " array([0.22807, 0.16173, 0.07255, 0.     , 0.37477, 0.06325, 0.09962]),\n",
       " array([0.36371, 0.01767, 0.04574, 0.     , 0.     , 0.09179, 0.48109]),\n",
       " array([0.13096, 0.26927, 0.06997, 0.00117, 0.19591, 0.06219, 0.27054]),\n",
       " array([0.21698, 0.17454, 0.05159, 0.00119, 0.18488, 0.08164, 0.28918]),\n",
       " array([0.02466, 0.38713, 0.0606 , 0.00229, 0.3709 , 0.06741, 0.087  ]),\n",
       " array([0.     , 0.413  , 0.39404, 0.     , 0.14083, 0.05212, 0.     ]),\n",
       " array([0.36764, 0.00662, 0.10346, 0.     , 0.24746, 0.03706, 0.23775]),\n",
       " array([0.16151, 0.24072, 0.06479, 0.     , 0.44992, 0.069  , 0.01406]),\n",
       " array([0.21709, 0.17463, 0.04604, 0.00296, 0.4411 , 0.08498, 0.0332 ]),\n",
       " array([0.35669, 0.01986, 0.05712, 0.00121, 0.1311 , 0.07957, 0.35444]),\n",
       " array([0.22673, 0.16423, 0.03426, 0.00297, 0.22344, 0.0964 , 0.25197]),\n",
       " array([0.09988, 0.30352, 0.0791 , 0.0029 , 0.45908, 0.05088, 0.00464]),\n",
       " array([0.13082, 0.26898, 0.09045, 0.00175, 0.45662, 0.04206, 0.00932]),\n",
       " array([0.10902, 0.29824, 0.0656 , 0.     , 0.40891, 0.06712, 0.05111]),\n",
       " array([0.31861, 0.0613 , 0.08924, 0.00241, 0.17338, 0.04682, 0.30824]),\n",
       " array([0.01938, 0.39804, 0.03839, 0.00172, 0.3664 , 0.08906, 0.08702]),\n",
       " array([0.37041, 0.00442, 0.06724, 0.00061, 0.02432, 0.07093, 0.46207]),\n",
       " array([0.07821, 0.32772, 0.06321, 0.     , 0.42982, 0.06868, 0.03235]),\n",
       " array([0.11447, 0.29212, 0.07044, 0.00058, 0.25577, 0.062  , 0.20461]),\n",
       " array([0.05138, 0.35803, 0.03726, 0.00058, 0.4422 , 0.09213, 0.01843]),\n",
       " array([0.01054, 0.40279, 0.05643, 0.00114, 0.4385 , 0.07232, 0.01827]),\n",
       " array([0.38521, 0.00442, 0.06226, 0.00304, 0.15573, 0.073  , 0.31633]),\n",
       " array([0.00176, 0.42257, 0.03374, 0.00057, 0.06399, 0.47737, 0.     ]),\n",
       " array([0.24523, 0.14274, 0.07277, 0.     , 0.07613, 0.06344, 0.39968]),\n",
       " array([0.00175, 0.46192, 0.09936, 0.00114, 0.36424, 0.05792, 0.01366]),\n",
       " array([0.     , 0.41532, 0.04878, 0.     , 0.09594, 0.43997, 0.     ]),\n",
       " array([0.22601, 0.16371, 0.0892 , 0.00178, 0.36963, 0.04541, 0.10426]),\n",
       " array([0.37439, 0.     , 0.06656, 0.     , 0.     , 0.07233, 0.48671]),\n",
       " array([0.39968, 0.00442, 0.0694 , 0.00365, 0.01459, 0.06554, 0.44271]),\n",
       " array([0.82819, 0.     , 0.04157, 0.     , 0.     , 0.09612, 0.03412]),\n",
       " array([0.00526, 0.40821, 0.07374, 0.00057, 0.4194 , 0.05635, 0.03647]),\n",
       " array([0.02469, 0.38761, 0.03978, 0.00287, 0.43095, 0.0866 , 0.02751]),\n",
       " array([0.     , 0.42441, 0.01881, 0.     , 0.43853, 0.11826, 0.     ]),\n",
       " array([0.34068, 0.0374 , 0.06193, 0.     , 0.00484, 0.07597, 0.47919]),\n",
       " array([0.30368, 0.07878, 0.05664, 0.00722, 0.03852, 0.07222, 0.44294]),\n",
       " array([0.     , 0.41414, 0.08589, 0.     , 0.43278, 0.06264, 0.00456]),\n",
       " array([0.14972, 0.24943, 0.02897, 0.00059, 0.45026, 0.10227, 0.01876]),\n",
       " array([0.36444, 0.01104, 0.06646, 0.00061, 0.03887, 0.07154, 0.44704]),\n",
       " array([0.17168, 0.22426, 0.06841, 0.00117, 0.09397, 0.06461, 0.3759 ]),\n",
       " array([0.35837, 0.01765, 0.0785 , 0.00425, 0.43191, 0.05594, 0.05338]),\n",
       " array([0.06393, 0.34421, 0.04006, 0.00346, 0.37402, 0.08658, 0.08773]),\n",
       " array([0.40246, 0.00222, 0.03012, 0.00122, 0.     , 0.10565, 0.45834]),\n",
       " array([0.00701, 0.49712, 0.08511, 0.     , 0.34633, 0.0462 , 0.01823]),\n",
       " array([0.01583, 0.41084, 0.04641, 0.     , 0.39795, 0.11067, 0.0183 ]),\n",
       " array([0.     , 0.42334, 0.23948, 0.00114, 0.12758, 0.08999, 0.11847]),\n",
       " array([0.00175, 0.41181, 0.09038, 0.00285, 0.45072, 0.03794, 0.00455]),\n",
       " array([0.40926, 0.     , 0.03299, 0.00061, 0.45352, 0.10363, 0.     ]),\n",
       " array([0.00175, 0.48825, 0.1605 , 0.     , 0.04544, 0.01325, 0.2908 ]),\n",
       " array([0.02284, 0.46586, 0.09739, 0.00057, 0.31514, 0.03425, 0.06394]),\n",
       " array([0.36845, 0.00663, 0.06651, 0.00122, 0.17506, 0.07092, 0.31122]),\n",
       " array([0.02645, 0.39018, 0.04248, 0.00287, 0.45396, 0.08407, 0.     ]),\n",
       " array([0.42545, 0.00221, 0.0872 , 0.00061, 0.     , 0.05198, 0.43256]),\n",
       " array([0.04429, 0.37099, 0.01422, 0.     , 0.12897, 0.40929, 0.03224]),\n",
       " array([0.08012, 0.32616, 0.03949, 0.     , 0.45367, 0.09593, 0.00463]),\n",
       " array([0.36773, 0.00662, 0.09921, 0.     , 0.06309, 0.04112, 0.42224]),\n",
       " array([0.0946 , 0.3101 , 0.04573, 0.00232, 0.45946, 0.08315, 0.00464]),\n",
       " array([0.00879, 0.40979, 0.03751, 0.     , 0.37945, 0.15988, 0.00457]),\n",
       " array([0.01054, 0.40276, 0.06247, 0.00343, 0.4476 , 0.06407, 0.00913]),\n",
       " array([0.29571, 0.08737, 0.05724, 0.0024 , 0.31234, 0.07675, 0.16818]),\n",
       " array([0.33325, 0.05044, 0.08443, 0.     , 0.42454, 0.05427, 0.05307]),\n",
       " array([0.00876, 0.40389, 0.43945, 0.00057, 0.02734, 0.11999, 0.     ]),\n",
       " array([0.09279, 0.3121 , 0.04639, 0.00232, 0.45465, 0.08248, 0.00928]),\n",
       " array([0.01057, 0.4039 , 0.00472, 0.     , 0.40765, 0.16858, 0.00458]),\n",
       " array([0.00176, 0.44531, 0.01882, 0.     , 0.34271, 0.10916, 0.08225]),\n",
       " array([0.00881, 0.41046, 0.00795, 0.     , 0.05495, 0.51783, 0.     ]),\n",
       " array([0.36467, 0.01542, 0.09766, 0.00061, 0.08725, 0.04174, 0.39264]),\n",
       " array([0.00527, 0.41388, 0.02554, 0.00057, 0.45252, 0.10221, 0.     ]),\n",
       " array([0.36198, 0.01323, 0.08846, 0.     , 0.33959, 0.05121, 0.14554]),\n",
       " array([0.31296, 0.06784, 0.07151, 0.     , 0.00963, 0.0662 , 0.47185]),\n",
       " array([0.06559, 0.34146, 0.0732 , 0.     , 0.32261, 0.05889, 0.13826]),\n",
       " array([0.3664 , 0.00884, 0.07148, 0.00243, 0.4666 , 0.06481, 0.01944]),\n",
       " array([0.04611, 0.36468, 0.00068, 0.     , 0.03689, 0.55164, 0.     ]),\n",
       " array([0.00175, 0.42952, 0.10566, 0.     , 0.35015, 0.02653, 0.0864 ]),\n",
       " array([0.12184, 0.27951, 0.06234, 0.00058, 0.23758, 0.06988, 0.22827]),\n",
       " array([0.2281 , 0.16174, 0.07047, 0.     , 0.40803, 0.06524, 0.06642]),\n",
       " array([0.05682, 0.35252, 0.02037, 0.00289, 0.43855, 0.10579, 0.02308]),\n",
       " array([0.     , 0.41317, 0.43363, 0.00057, 0.03636, 0.11173, 0.00454]),\n",
       " array([0.30845, 0.07203, 0.10593, 0.     , 0.47543, 0.03335, 0.0048 ]),\n",
       " array([0.00351, 0.41537, 0.04497, 0.00057, 0.44276, 0.08368, 0.00913]),\n",
       " array([0.02822, 0.38352, 0.04653, 0.00229, 0.44938, 0.08088, 0.00917]),\n",
       " array([0.01408, 0.39935, 0.03701, 0.00172, 0.42098, 0.09025, 0.03661]),\n",
       " array([0.01053, 0.40255, 0.06847, 0.00171, 0.38346, 0.0648 , 0.06847]),\n",
       " array([0.85107, 0.     , 0.07298, 0.     , 0.     , 0.06622, 0.00973]),\n",
       " array([0.04961, 0.36014, 0.03319, 0.00173, 0.36852, 0.09469, 0.09213]),\n",
       " array([0.37174, 0.00885, 0.05439, 0.00548, 0.12167, 0.07773, 0.36014]),\n",
       " array([0.00702, 0.42489, 0.05638, 0.     , 0.27842, 0.0781 , 0.15518]),\n",
       " array([0.3481 , 0.0286 , 0.09253, 0.00242, 0.06776, 0.04437, 0.41623]),\n",
       " array([0.37005, 0.00442, 0.08932, 0.00304, 0.04859, 0.04724, 0.43733]),\n",
       " array([0.07091, 0.33523, 0.09219, 0.0023 , 0.04609, 0.03841, 0.41485]),\n",
       " array([0.00351, 0.41034, 0.36984, 0.     , 0.00456, 0.21175, 0.     ]),\n",
       " array([0.     , 0.41334, 0.10565, 0.00171, 0.08184, 0.02463, 0.37284]),\n",
       " array([0.07838, 0.33768, 0.02997, 0.00232, 0.12042, 0.09778, 0.33347]),\n",
       " array([0.01052, 0.40644, 0.0945 , 0.     , 0.19597, 0.03735, 0.25522]),\n",
       " array([0.15868, 0.23867, 0.06481, 0.     , 0.45007, 0.06902, 0.01875]),\n",
       " array([0.42853, 0.     , 0.04157, 0.     , 0.00975, 0.09612, 0.42403]),\n",
       " array([0.07461, 0.33172, 0.06385, 0.00058, 0.45265, 0.06736, 0.00924]),\n",
       " array([0.37144, 0.00884, 0.07151, 0.00669, 0.17992, 0.06011, 0.30149]),\n",
       " array([0.02467, 0.38736, 0.05121, 0.00286, 0.43068, 0.07573, 0.02749]),\n",
       " array([0.01406, 0.39867, 0.06651, 0.00114, 0.44768, 0.06281, 0.00914]),\n",
       " array([0.36122, 0.01548, 0.03077, 0.00182, 0.17518, 0.10408, 0.31144]),\n",
       " array([0.01756, 0.39441, 0.08529, 0.00228, 0.31511, 0.04377, 0.14157]),\n",
       " array([0.0035 , 0.43221, 0.10504, 0.00114, 0.41856, 0.02591, 0.01365]),\n",
       " array([0.01581, 0.39665, 0.06853, 0.00057, 0.45231, 0.06155, 0.00457]),\n",
       " array([0.0088 , 0.40556, 0.01884, 0.00057, 0.44841, 0.11325, 0.00458]),\n",
       " array([0.05832, 0.34882, 0.10339, 0.00057, 0.32167, 0.02936, 0.13786]),\n",
       " array([0.06926, 0.33788, 0.05703, 0.00231, 0.0277 , 0.07182, 0.434  ]),\n",
       " array([0.86151, 0.     , 0.05944, 0.00061, 0.     , 0.07845, 0.     ]),\n",
       " array([0.07114, 0.33629, 0.03808, 0.00347, 0.45315, 0.08863, 0.00925]),\n",
       " array([0.36799, 0.00662, 0.08999, 0.00182, 0.     , 0.04789, 0.48568]),\n",
       " array([0.36705, 0.03523, 0.05913, 0.00303, 0.01453, 0.07536, 0.44568]),\n",
       " array([0.     , 0.427  , 0.13801, 0.     , 0.36375, 0.03031, 0.04092]),\n",
       " array([0.00529, 0.41032, 0.01199, 0.05499, 0.     , 0.51741, 0.     ]),\n",
       " array([0.36278, 0.01326, 0.04862, 0.     , 0.45703, 0.08914, 0.02917]),\n",
       " array([0.13652, 0.26325, 0.06662, 0.00234, 0.07006, 0.06422, 0.39699]),\n",
       " array([0.00351, 0.41063, 0.06079, 0.     , 0.43344, 0.09163, 0.     ]),\n",
       " array([0.25283, 0.13424, 0.07705, 0.     , 0.47157, 0.05954, 0.00476]),\n",
       " array([0.06373, 0.36154, 0.09206, 0.00115, 0.44188, 0.03964, 0.     ]),\n",
       " array([0.84762, 0.     , 0.043  , 0.     , 0.     , 0.09476, 0.01462]),\n",
       " array([0.03174, 0.37928, 0.05798, 0.00057, 0.13754, 0.07195, 0.32093]),\n",
       " array([0.47564, 0.01103, 0.08993, 0.00182, 0.10678, 0.04786, 0.26694]),\n",
       " array([0.3563 , 0.01984, 0.07418, 0.     , 0.40741, 0.06467, 0.0776 ]),\n",
       " array([0.32284, 0.05701, 0.08087, 0.00663, 0.00965, 0.05025, 0.47275]),\n",
       " array([0.02467, 0.38733, 0.05174, 0.     , 0.44438, 0.0873 , 0.00458]),\n",
       " array([0.     , 0.41527, 0.02929, 0.00057, 0.04568, 0.5092 , 0.     ]),\n",
       " array([0.35042, 0.02643, 0.07127, 0.00061, 0.3247 , 0.06664, 0.15993]),\n",
       " array([0.37048, 0.00442, 0.06367, 0.00061, 0.45244, 0.07432, 0.03405]),\n",
       " array([0.35486, 0.02207, 0.05142, 0.00243, 0.05342, 0.08363, 0.43218]),\n",
       " array([0.02468, 0.38749, 0.04516, 0.00286, 0.39416, 0.08148, 0.06417]),\n",
       " array([0.35445, 0.02205, 0.06848, 0.00061, 0.11641, 0.06939, 0.36863]),\n",
       " array([0.28195, 0.10236, 0.06623, 0.0018 , 0.35455, 0.06854, 0.12457]),\n",
       " array([0.00701, 0.40584, 0.09379, 0.00114, 0.45553, 0.0367 , 0.     ]),\n",
       " array([0.38849, 0.00664, 0.03796, 0.00061, 0.12663, 0.09876, 0.34092]),\n",
       " array([0.06568, 0.34195, 0.0509 , 0.00173, 0.01846, 0.0782 , 0.44307]),\n",
       " array([0.30722, 0.07436, 0.06439, 0.     , 0.39457, 0.07285, 0.08661]),\n",
       " array([0.36276, 0.01326, 0.05076, 0.00061, 0.27226, 0.08643, 0.21392]),\n",
       " array([0.34232, 0.03518, 0.07967, 0.     , 0.00484, 0.05912, 0.47887]),\n",
       " array([0.37328, 0.00663, 0.06578, 0.     , 0.33064, 0.07294, 0.15073]),\n",
       " array([0.     , 0.41567, 0.00592, 0.     , 0.02286, 0.55555, 0.     ]),\n",
       " array([0.03526, 0.375  , 0.07482, 0.00057, 0.12375, 0.05602, 0.33458]),\n",
       " array([0.35315, 0.02429, 0.03215, 0.     , 0.31578, 0.10459, 0.17004]),\n",
       " array([0.34039, 0.03737, 0.07823, 0.0006 , 0.06771, 0.05978, 0.41591]),\n",
       " array([0.02113, 0.39126, 0.05185, 0.00286, 0.42123, 0.07504, 0.03663]),\n",
       " array([0.35559, 0.0198 , 0.10962, 0.     , 0.00484, 0.03093, 0.47921]),\n",
       " array([0.36212, 0.01324, 0.08136, 0.     , 0.01941, 0.05797, 0.4659 ]),\n",
       " array([0.0176 , 0.4134 , 0.05046, 0.00057, 0.42089, 0.07879, 0.0183 ]),\n",
       " array([0.07276, 0.33346, 0.07599, 0.     , 0.41987, 0.05639, 0.04153]),\n",
       " array([0.     , 0.44573, 0.09105, 0.00569, 0.39152, 0.03414, 0.03187]),\n",
       " array([0.07099, 0.33557, 0.07328, 0.00173, 0.16611, 0.05703, 0.2953 ]),\n",
       " array([0.00175, 0.41157, 0.10412, 0.     , 0.42771, 0.03665, 0.0182 ]),\n",
       " array([0.0035 , 0.40974, 0.09373, 0.00171, 0.33235, 0.03604, 0.12292]),\n",
       " array([0.25398, 0.1386 , 0.06726, 0.00298, 0.32874, 0.06551, 0.14293]),\n",
       " array([0.05479, 0.35303, 0.08921, 0.     , 0.04136, 0.0434 , 0.4182 ]),\n",
       " array([0.42996, 0.00884, 0.05222, 0.00061, 0.04865, 0.08513, 0.37458]),\n",
       " array([0.36459, 0.01105, 0.0579 , 0.     , 0.44723, 0.08034, 0.03889]),\n",
       " array([0.0637 , 0.34298, 0.09878, 0.00058, 0.41408, 0.03387, 0.04601]),\n",
       " array([0.01579, 0.40058, 0.09593, 0.     , 0.37861, 0.03611, 0.07298]),\n",
       " array([0.00351, 0.43307, 0.08071, 0.     , 0.40572, 0.06331, 0.01368]),\n",
       " array([0.3405 , 0.03738, 0.07114, 0.     , 0.00484, 0.06719, 0.47894]),\n",
       " array([0.01759, 0.39492, 0.06456, 0.00343, 0.30637, 0.06224, 0.1509 ]),\n",
       " array([0.04773, 0.3614 , 0.06623, 0.00057, 0.17464, 0.06447, 0.28495]),\n",
       " array([0.28959, 0.09374, 0.06982, 0.     , 0.43642, 0.06727, 0.04316]),\n",
       " array([0.00879, 0.40496, 0.0477 , 0.00171, 0.45688, 0.07995, 0.     ]),\n",
       " array([0.37826, 0.00222, 0.02654, 0.00366, 0.     , 0.10637, 0.48295]),\n",
       " array([0.34863, 0.02864, 0.06059, 0.     , 0.03393, 0.07742, 0.45079]),\n",
       " array([0.     , 0.4154 , 0.08305, 0.08053, 0.00457, 0.41645, 0.     ]),\n",
       " array([0.35671, 0.01986, 0.05356, 0.     , 0.43701, 0.0843 , 0.04856]),\n",
       " array([0.1109 , 0.29171, 0.05813, 0.00058, 0.32088, 0.07363, 0.14416]),\n",
       " array([0.02466, 0.38718, 0.05388, 0.     , 0.04122, 0.08549, 0.40758]),\n",
       " array([0.0035 , 0.41393, 0.14154, 0.00171, 0.40483, 0.02085, 0.01365]),\n",
       " array([0.41856, 0.     , 0.05588, 0.00244, 0.     , 0.07984, 0.44329]),\n",
       " array([0.37567, 0.00443, 0.04798, 0.00061, 0.     , 0.08927, 0.48205]),\n",
       " array([0.37698, 0.00221, 0.08723, 0.00365, 0.00486, 0.04862, 0.47646]),\n",
       " array([0.00352, 0.43007, 0.00202, 0.     , 0.20588, 0.35394, 0.00458]),\n",
       " array([0.00175, 0.53405, 0.11675, 0.     , 0.31373, 0.02463, 0.00909]),\n",
       " array([0.47587, 0.00664, 0.04867, 0.00061, 0.0292 , 0.08856, 0.35045]),\n",
       " array([0.30736, 0.0744 , 0.05734, 0.0006 , 0.11072, 0.0789 , 0.37068]),\n",
       " array([0.01761, 0.39538, 0.0377 , 0.00057, 0.43034, 0.09093, 0.02747]),\n",
       " array([0.06744, 0.3398 , 0.05972, 0.00173, 0.03692, 0.06986, 0.42454]),\n",
       " array([0.52609, 0.00883, 0.09208, 0.00061, 0.04368, 0.04719, 0.28153]),\n",
       " array([0.06212, 0.34612, 0.05226, 0.0075 , 0.21229, 0.07051, 0.24921]),\n",
       " array([0.49132, 0.     , 0.06372, 0.00061, 0.00487, 0.07438, 0.36512]),\n",
       " array([0.36376, 0.01102, 0.09843, 0.     , 0.09215, 0.04177, 0.39286]),\n",
       " array([0.11818, 0.28357, 0.06025, 0.     , 0.00931, 0.07242, 0.45626]),\n",
       " array([0.03181, 0.40304, 0.02162, 0.00115, 0.42267, 0.11052, 0.00919]),\n",
       " array([0.00351, 0.43347, 0.05033, 0.     , 0.43347, 0.07922, 0.     ]),\n",
       " array([0.25522, 0.13237, 0.04704, 0.00179, 0.01432, 0.0862 , 0.46307]),\n",
       " array([0.00352, 0.41193, 0.01198, 0.03376, 0.     , 0.53881, 0.     ]),\n",
       " array([0.04598, 0.36367, 0.0541 , 0.00172, 0.45521, 0.07472, 0.0046 ]),\n",
       " array([0.3306 , 0.04829, 0.07315, 0.     , 0.     , 0.06506, 0.4829 ]),\n",
       " array([0.01229, 0.40061, 0.06581, 0.00057, 0.45209, 0.06406, 0.00457]),\n",
       " array([0.10872, 0.29279, 0.11449, 0.     , 0.19463, 0.0206 , 0.26878]),\n",
       " array([0.37747, 0.00221, 0.05656, 0.     , 0.02921, 0.08181, 0.45274]),\n",
       " array([0.36508, 0.01544, 0.07993, 0.00182, 0.02426, 0.05729, 0.45617]),\n",
       " array([0.0035 , 0.40981, 0.09107, 0.00171, 0.10928, 0.03858, 0.34606]),\n",
       " array([0.76535, 0.     , 0.03299, 0.00183, 0.     , 0.10229, 0.09754]),\n",
       " array([0.82626, 0.00222, 0.03655, 0.     , 0.     , 0.10086, 0.03412]),\n",
       " array([0.00526, 0.41257, 0.39226, 0.00057, 0.01367, 0.17568, 0.     ]),\n",
       " array([0.02466, 0.38714, 0.05589, 0.     , 0.45333, 0.07441, 0.00458]),\n",
       " array([0.01761, 0.39534, 0.03837, 0.     , 0.27466, 0.09092, 0.18311]),\n",
       " array([0.00175, 0.41176, 0.31008, 0.     , 0.05007, 0.22634, 0.     ]),\n",
       " array([0.36435, 0.01104, 0.07215, 0.00121, 0.04372, 0.06545, 0.44207]),\n",
       " array([0.33873, 0.03959, 0.06049, 0.     , 0.00968, 0.07729, 0.47422]),\n",
       " array([0.02819, 0.40151, 0.05862, 0.00057, 0.16494, 0.07127, 0.2749 ]),\n",
       " array([0.007  , 0.40529, 0.55246, 0.     , 0.     , 0.03526, 0.     ]),\n",
       " array([0.35696, 0.01988, 0.04216, 0.00061, 0.30612, 0.09448, 0.17979]),\n",
       " array([0.36427, 0.01104, 0.07357, 0.     , 0.01943, 0.06543, 0.46626]),\n",
       " array([0.00176, 0.4131 , 0.14507, 0.00571, 0.0137 , 0.42067, 0.     ]),\n",
       " array([0.01941, 0.3942 , 0.01512, 0.     , 0.00918, 0.5621 , 0.     ]),\n",
       " array([0.10193, 0.30222, 0.03966, 0.00407, 0.4417 , 0.08718, 0.02325]),\n",
       " array([0.36244, 0.01325, 0.06572, 0.     , 0.11658, 0.07286, 0.36916]),\n",
       " array([0.24552, 0.14292, 0.05605, 0.00238, 0.01906, 0.07675, 0.45733]),\n",
       " array([0.07822, 0.32776, 0.06118, 0.     , 0.0416 , 0.07062, 0.42062]),\n",
       " array([0.22644, 0.16402, 0.05237, 0.00059, 0.16143, 0.08177, 0.31337]),\n",
       " array([0.01057, 0.40376, 0.06478, 0.01087, 0.23809, 0.27193, 0.     ]),\n",
       " array([0.02289, 0.38909, 0.05856, 0.00114, 0.325  , 0.07057, 0.13275]),\n",
       " array([0.00526, 0.42184, 0.07776, 0.00171, 0.42392, 0.05128, 0.01823]),\n",
       " array([0.12563, 0.27574, 0.04392, 0.00058, 0.37331, 0.08749, 0.09333]),\n",
       " array([0.3598 , 0.02208, 0.04214, 0.     , 0.01943, 0.09512, 0.46143]),\n",
       " array([0.37517, 0.00442, 0.07222, 0.00061, 0.01945, 0.06619, 0.46195]),\n",
       " array([0.79303, 0.     , 0.06514, 0.     , 0.00487, 0.07369, 0.06328]),\n",
       " array([0.02643, 0.38525, 0.0539 , 0.00057, 0.05039, 0.07572, 0.40774]),\n",
       " array([0.37454, 0.     , 0.05943, 0.     , 0.00487, 0.07912, 0.48204]),\n",
       " array([0.07101, 0.33567, 0.06448, 0.     , 0.15692, 0.06731, 0.30462]),\n",
       " array([0.02288, 0.38903, 0.0599 , 0.00057, 0.44395, 0.06992, 0.01373]),\n",
       " array([0.35269, 0.02426, 0.05565, 0.     , 0.42696, 0.08221, 0.05822]),\n",
       " array([0.00526, 0.40847, 0.29046, 0.00057, 0.01368, 0.28155, 0.     ]),\n",
       " array([0.007  , 0.41886, 0.11906, 0.     , 0.39116, 0.0139 , 0.05003]),\n",
       " array([0.43653, 0.00222, 0.0337 , 0.00183, 0.01463, 0.10157, 0.40953]),\n",
       " array([0.00527, 0.40931, 0.09411, 0.00057, 0.15541, 0.33076, 0.00457]),\n",
       " array([0.11459, 0.28778, 0.05134, 0.00058, 0.34449, 0.08017, 0.12104]),\n",
       " array([0.2588 , 0.12798, 0.06878, 0.00358, 0.0859 , 0.06363, 0.39133]),\n",
       " array([0.37576, 0.00443, 0.04369, 0.00061, 0.     , 0.09335, 0.48216]),\n",
       " array([0.36383, 0.01103, 0.09631, 0.00061, 0.03881, 0.04312, 0.4463 ]),\n",
       " array([0.0123 , 0.40071, 0.06113, 0.00057, 0.44764, 0.06852, 0.00914]),\n",
       " array([0.37284, 0.01764, 0.0742 , 0.     , 0.0097 , 0.06469, 0.46092]),\n",
       " array([0.85978, 0.00221, 0.05013, 0.00061, 0.     , 0.08726, 0.     ]),\n",
       " array([0.00703, 0.40681, 0.06111, 0.00628, 0.24658, 0.06215, 0.21005]),\n",
       " array([0.12008, 0.28171, 0.05003, 0.     , 0.35415, 0.0822 , 0.11184]),\n",
       " array([0.38031, 0.01983, 0.0891 , 0.00182, 0.14541, 0.04847, 0.31506]),\n",
       " array([0.00176, 0.41732, 0.04697, 0.00171, 0.11864, 0.08049, 0.33311]),\n",
       " array([0.00525, 0.41682, 0.09642, 0.00114, 0.43255, 0.03415, 0.01366]),\n",
       " array([0.28943, 0.09368, 0.08458, 0.0024 , 0.19172, 0.05059, 0.28759]),\n",
       " array([0.19419, 0.19947, 0.06107, 0.     , 0.27368, 0.0734 , 0.19818]),\n",
       " array([0.36099, 0.01547, 0.04219, 0.00182, 0.1313 , 0.09321, 0.35501]),\n",
       " array([0.00526, 0.43579, 0.06305, 0.     , 0.3284 , 0.06715, 0.10034]),\n",
       " array([0.39463, 0.     , 0.03299, 0.00061, 0.32673, 0.10363, 0.14142]),\n",
       " array([0.31355, 0.06797, 0.04043, 0.00121, 0.39555, 0.09447, 0.08683]),\n",
       " array([0.19628, 0.1976 , 0.04308, 0.00059, 0.31659, 0.08991, 0.15593]),\n",
       " array([0.     , 0.41439, 0.06837, 0.0057 , 0.45583, 0.05571, 0.     ]),\n",
       " array([0.0817 , 0.32325, 0.091  , 0.00058, 0.26784, 0.04169, 0.19395]),\n",
       " array([0.02465, 0.387  , 0.06462, 0.00114, 0.07324, 0.06485, 0.3845 ]),\n",
       " array([0.12202, 0.27994, 0.03362, 0.     , 0.46657, 0.09785, 0.     ]),\n",
       " array([0.     , 0.50113, 0.06571, 0.00684, 0.36932, 0.05699, 0.     ]),\n",
       " array([0.31318, 0.06789, 0.07227, 0.00662, 0.38063, 0.05822, 0.10118]),\n",
       " array([0.00703, 0.40724, 0.03415, 0.     , 0.43882, 0.10361, 0.00914]),\n",
       " array([0.00702, 0.40673, 0.11172, 0.     , 0.3287 , 0.14127, 0.00457]),\n",
       " array([0.00704, 0.40788, 0.01979, 0.00229, 0.     , 0.56299, 0.     ]),\n",
       " array([0.38065, 0.00443, 0.04441, 0.00122, 0.     , 0.092  , 0.4773 ]),\n",
       " array([0.01931, 0.39211, 0.098  , 0.00057, 0.44729, 0.0336 , 0.00913]),\n",
       " array([0.36449, 0.01105, 0.06504, 0.00121, 0.44225, 0.07222, 0.04374]),\n",
       " array([0.36999, 0.00442, 0.09002, 0.00182, 0.03887, 0.04791, 0.44697]),\n",
       " array([0.0353 , 0.37542, 0.05466, 0.00057, 0.40838, 0.0752 , 0.05047]),\n",
       " array([0.36386, 0.01103, 0.09846, 0.00243, 0.08248, 0.03908, 0.40267]),\n",
       " array([0.81849, 0.     , 0.04157, 0.00061, 0.     , 0.09545, 0.04387]),\n",
       " array([0.32088, 0.05918, 0.06737, 0.     , 0.01447, 0.07033, 0.46777]),\n",
       " array([0.09269, 0.31177, 0.0627 , 0.00116, 0.19465, 0.06823, 0.2688 ]),\n",
       " array([0.11272, 0.28968, 0.05746, 0.     , 0.03256, 0.07495, 0.43262]),\n",
       " array([0.02466, 0.38723, 0.05523, 0.00172, 0.27939, 0.07315, 0.17862]),\n",
       " array([0.36495, 0.01544, 0.08275, 0.     , 0.05336, 0.0566 , 0.4269 ]),\n",
       " array([0.35635, 0.01984, 0.07134, 0.     , 0.44628, 0.06737, 0.03881]),\n",
       " array([0.27994, 0.10926, 0.0669 , 0.     , 0.45012, 0.06983, 0.02394]),\n",
       " array([0.     , 0.56154, 0.01814, 0.00114, 0.31067, 0.10851, 0.     ]),\n",
       " array([0.52785, 0.00221, 0.07581, 0.00061, 0.1848 , 0.06282, 0.1459 ]),\n",
       " array([0.00352, 0.41156, 0.04734, 0.     , 0.01372, 0.52386, 0.     ]),\n",
       " array([0.36251, 0.01325, 0.06216, 0.     , 0.27693, 0.07625, 0.20891]),\n",
       " array([0.36473, 0.01105, 0.05078, 0.     , 0.34042, 0.08713, 0.14589]),\n",
       " array([0.00701, 0.41069, 0.07844, 0.     , 0.23705, 0.05711, 0.2097 ]),\n",
       " array([0.22047, 0.17011, 0.07385, 0.     , 0.00947, 0.06185, 0.46425]),\n",
       " array([0.01232, 0.4152 , 0.04105, 0.00744, 0.19678, 0.08009, 0.24712]),\n",
       " array([0.45263, 0.     , 0.05229, 0.     , 0.     , 0.08592, 0.40916]),\n",
       " array([0.36655, 0.00884, 0.05935, 0.     , 0.     , 0.07901, 0.48624]),\n",
       " array([0.0639 , 0.34401, 0.04818, 0.00231, 0.46148, 0.08012, 0.     ]),\n",
       " array([0.01934, 0.39265, 0.07192, 0.     , 0.39307, 0.05904, 0.06399]),\n",
       " array([0.34716, 0.03088, 0.03854, 0.00243, 0.48043, 0.09571, 0.00485]),\n",
       " array([0.01582, 0.39686, 0.0605 , 0.00114, 0.44797, 0.06857, 0.00914]),\n",
       " array([0.37752, 0.00221, 0.058  , 0.00183, 0.     , 0.07844, 0.48201]),\n",
       " array([0.01053, 0.4022 , 0.08384, 0.00171, 0.2463 , 0.04561, 0.20981]),\n",
       " array([0.00176, 0.42679, 0.03479, 0.00057, 0.32881, 0.10681, 0.10047]),\n",
       " array([0.37923, 0.     , 0.06799, 0.     , 0.     , 0.07097, 0.48181]),\n",
       " array([0.44613, 0.00663, 0.0765 , 0.00365, 0.02431, 0.05874, 0.38405]),\n",
       " array([0.06384, 0.3437 , 0.06102, 0.00058, 0.44723, 0.0698 , 0.01383]),\n",
       " array([0.3708 , 0.00883, 0.09495, 0.00303, 0.     , 0.0418 , 0.48059]),\n",
       " array([0.12553, 0.27552, 0.06034, 0.00175, 0.01399, 0.07059, 0.45228]),\n",
       " array([0.37448, 0.     , 0.06229, 0.     , 0.41867, 0.0764 , 0.06816]),\n",
       " array([0.06557, 0.34137, 0.07792, 0.     , 0.44232, 0.05439, 0.01843]),\n",
       " array([0.37828, 0.00663, 0.05937, 0.     , 0.04864, 0.07904, 0.42804]),\n",
       " array([0.01931, 0.3921 , 0.09732, 0.     , 0.45184, 0.03486, 0.00456]),\n",
       " array([0.11282, 0.28994, 0.04177, 0.     , 0.13502, 0.08989, 0.33057]),\n",
       " array([0.37897, 0.     , 0.0801 , 0.     , 0.03404, 0.05944, 0.44744]),\n",
       " array([0.00175, 0.44407, 0.06902, 0.     , 0.30985, 0.06139, 0.11392]),\n",
       " array([0.38272, 0.01328, 0.0315 , 0.00304, 0.03408, 0.1021 , 0.43328]),\n",
       " array([0.4819 , 0.     , 0.05158, 0.00061, 0.01949, 0.08593, 0.36049]),\n",
       " array([0.83762, 0.     , 0.04944, 0.00061, 0.     , 0.08797, 0.02436]),\n",
       " array([0.09076, 0.31336, 0.08437, 0.     , 0.0694 , 0.04884, 0.39328]),\n",
       " array([0.05847, 0.34968, 0.06029, 0.00115, 0.35471, 0.06974, 0.10595]),\n",
       " array([0.34728, 0.03089, 0.02998, 0.00121, 0.11165, 0.10518, 0.3738 ]),\n",
       " array([0.37213, 0.00221, 0.07936, 0.     , 0.05834, 0.0601 , 0.42785]),\n",
       " array([0.75521, 0.     , 0.04086, 0.00122, 0.     , 0.09546, 0.10724]),\n",
       " array([0.27636, 0.10887, 0.04861, 0.     , 0.43111, 0.08715, 0.0479 ]),\n",
       " array([0.37143, 0.00884, 0.06221, 0.00182, 0.42791, 0.07429, 0.05349]),\n",
       " array([0.30712, 0.07434, 0.07145, 0.0006 , 0.0481 , 0.06547, 0.43292]),\n",
       " array([0.02646, 0.38564, 0.04114, 0.00344, 0.44943, 0.08471, 0.00917]),\n",
       " array([0.0035 , 0.41448, 0.08574, 0.00171, 0.44637, 0.04365, 0.00455]),\n",
       " array([0.01938, 0.39351, 0.03503, 0.00115, 0.43973, 0.09288, 0.01832]),\n",
       " array([0.04416, 0.36991, 0.06889, 0.00115, 0.43629, 0.06123, 0.01837]),\n",
       " array([0.10913, 0.2939 , 0.04788, 0.00058, 0.06977, 0.08334, 0.39539]),\n",
       " array([0.36204, 0.01323, 0.08634, 0.00061, 0.0097 , 0.05256, 0.47551]),\n",
       " array([0.08919, 0.3162 , 0.03751, 0.00058, 0.44058, 0.09275, 0.02319]),\n",
       " array([0.36285, 0.01763, 0.0884 , 0.     , 0.33936, 0.05117, 0.14059]),\n",
       " array([0.     , 0.41406, 0.07569, 0.00228, 0.37348, 0.05251, 0.08198]),\n",
       " array([0.01228, 0.40021, 0.08386, 0.00057, 0.42882, 0.04689, 0.02737]),\n",
       " array([0.09466, 0.31029, 0.03346, 0.00174, 0.45045, 0.09546, 0.01393]),\n",
       " array([0.37449, 0.     , 0.063  , 0.00061, 0.00487, 0.07505, 0.48197]),\n",
       " array([0.13829, 0.26107, 0.07142, 0.     , 0.21947, 0.06226, 0.24749]),\n",
       " array([0.35383, 0.02201, 0.09969, 0.00061, 0.11136, 0.03968, 0.37283]),\n",
       " array([0.0088 , 0.41006, 0.02355, 0.00114, 0.45289, 0.10357, 0.     ]),\n",
       " array([0.36821, 0.00663, 0.07575, 0.     , 0.4811 , 0.06345, 0.00486]),\n",
       " array([0.04055, 0.36876, 0.09167, 0.00057, 0.055  , 0.04011, 0.40334]),\n",
       " array([0.21096, 0.18054, 0.07858, 0.00059, 0.33099, 0.05648, 0.14185]),\n",
       " array([0.0088 , 0.40572, 0.07485, 0.00458, 0.0412 , 0.46485, 0.     ]),\n",
       " array([0.02291, 0.38948, 0.03908, 0.00057, 0.44905, 0.08973, 0.00916]),\n",
       " array([0.01406, 0.39878, 0.06182, 0.00114, 0.43865, 0.06727, 0.01828]),\n",
       " array([0.00528, 0.4096 , 0.04224, 0.01086, 0.16925, 0.36276, 0.     ]),\n",
       " array([0.     , 0.41512, 0.0732 , 0.00571, 0.     , 0.50598, 0.     ]),\n",
       " array([0.22076, 0.17034, 0.05092, 0.00059, 0.32257, 0.08301, 0.1518 ]),\n",
       " array([0.03176, 0.37952, 0.04992, 0.00229, 0.445  , 0.07773, 0.01376]),\n",
       " array([0.38943, 0.     , 0.04658, 0.     , 0.08283, 0.09136, 0.3898 ]),\n",
       " array([0.36258, 0.01325, 0.0586 , 0.     , 0.43734, 0.07964, 0.04859]),\n",
       " array([0.1164 , 0.28571, 0.05683, 0.00175, 0.31195, 0.07372, 0.15365]),\n",
       " array([0.01931, 0.40124, 0.10122, 0.     , 0.42448, 0.0355 , 0.01826]),\n",
       " array([0.3682 , 0.00663, 0.07646, 0.     , 0.22353, 0.06277, 0.26241]),\n",
       " array([0.37246, 0.00221, 0.06584, 0.00122, 0.09246, 0.07164, 0.39417]),\n",
       " array([0.10349, 0.29944, 0.07709, 0.     , 0.10206, 0.05606, 0.36186]),\n",
       " array([0.00352, 0.41161, 0.03161, 0.     , 0.00457, 0.54869, 0.     ]),\n",
       " array([0.00701, 0.41044, 0.1009 , 0.00114, 0.40091, 0.05227, 0.02734]),\n",
       " array([0.     , 0.41555, 0.00672, 0.     , 0.43425, 0.12062, 0.02286]),\n",
       " array([0.33717, 0.04183, 0.03846, 0.     , 0.34387, 0.09821, 0.14046]),\n",
       " array([0.     , 0.4148 , 0.35268, 0.0154 , 0.0365 , 0.18061, 0.     ]),\n",
       " array([0.12171, 0.27923, 0.07939, 0.     , 0.38161, 0.05429, 0.08377]),\n",
       " array([0.35322, 0.0243 , 0.03001, 0.00061, 0.35471, 0.10596, 0.1312 ]),\n",
       " array([0.01232, 0.40136, 0.03229, 0.00057, 0.39346, 0.09595, 0.06405]),\n",
       " array([0.00351, 0.42035, 0.0262 , 0.     , 0.05026, 0.10217, 0.3975 ]),\n",
       " array([0.0123 , 0.40533, 0.06113, 0.00171, 0.30151, 0.06726, 0.15075]),\n",
       " array([0.00175, 0.41175, 0.37394, 0.00228, 0.     , 0.21028, 0.     ]),\n",
       " array([0.36994, 0.00442, 0.08858, 0.     , 0.11173, 0.05128, 0.37405]),\n",
       " array([0.00176, 0.41365, 0.04586, 0.     , 0.00915, 0.52958, 0.     ]),\n",
       " array([0.08191, 0.32408, 0.04834, 0.00231, 0.45834, 0.08038, 0.00463]),\n",
       " array([0.05675, 0.35209, 0.0617 , 0.01268, 0.11066, 0.05571, 0.35041]),\n",
       " array([0.01405, 0.40304, 0.08232, 0.     , 0.43832, 0.06227, 0.     ]),\n",
       " array([0.02289, 0.38913, 0.05655, 0.00114, 0.37997, 0.07248, 0.07783]),\n",
       " array([0.     , 0.41966, 0.02753, 0.00057, 0.19177, 0.1002 , 0.26026]),\n",
       " array([0.35948, 0.01541, 0.11395, 0.     , 0.06295, 0.0269 , 0.42131]),\n",
       " array([0.36246, 0.01325, 0.06572, 0.00061, 0.     , 0.07219, 0.48577]),\n",
       " array([0.0123 , 0.40521, 0.06447, 0.00057, 0.45212, 0.06533, 0.     ]),\n",
       " array([0.01232, 0.40142, 0.02826, 0.     , 0.45301, 0.10041, 0.00458]),\n",
       " array([0.     , 0.42341, 0.06099, 0.     , 0.41471, 0.06899, 0.0319 ]),\n",
       " array([0.19574, 0.19706, 0.09494, 0.00177, 0.04712, 0.03927, 0.4241 ]),\n",
       " array([0.25284, 0.13425, 0.08407, 0.00417, 0.04764, 0.0483 , 0.42874]),\n",
       " array([0.36395, 0.01103, 0.0892 , 0.     , 0.44159, 0.05055, 0.04367]),\n",
       " array([0.34753, 0.02855, 0.11724, 0.     , 0.06281, 0.02349, 0.42038]),\n",
       " array([0.3473 , 0.03089, 0.03498, 0.00425, 0.01456, 0.0971 , 0.47091]),\n",
       " array([0.36764, 0.00662, 0.10346, 0.     , 0.25231, 0.03706, 0.2329 ]),\n",
       " array([0.39407, 0.     , 0.06302, 0.00304, 0.05357, 0.07237, 0.41393]),\n",
       " array([0.35036, 0.02643, 0.07482, 0.00061, 0.00969, 0.06326, 0.47484]),\n",
       " array([0.02115, 0.39161, 0.0337 , 0.00172, 0.45826, 0.09356, 0.     ]),\n",
       " array([0.     , 0.4154 , 0.09636, 0.00057, 0.01371, 0.47395, 0.     ]),\n",
       " array([0.37038, 0.00442, 0.06723, 0.     , 0.     , 0.0716 , 0.48636]),\n",
       " array([0.01406, 0.39878, 0.06048, 0.00057, 0.38383, 0.06918, 0.07311]),\n",
       " array([0.0371 , 0.37369, 0.04795, 0.00287, 0.43632, 0.0791 , 0.02296]),\n",
       " array([0.35412, 0.02203, 0.08409, 0.     , 0.     , 0.05519, 0.48458]),\n",
       " array([0.36588, 0.00882, 0.09707, 0.00243, 0.01456, 0.04045, 0.47079]),\n",
       " array([0.37201, 0.00221, 0.08506, 0.     , 0.27218, 0.05468, 0.21386]),\n",
       " array([0.09625, 0.30756, 0.07224, 0.     , 0.26416, 0.0605 , 0.19928]),\n",
       " array([0.36476, 0.01105, 0.04935, 0.     , 0.05836, 0.08849, 0.42799]),\n",
       " array([0.02287, 0.3888 , 0.06929, 0.     , 0.45742, 0.06162, 0.     ]),\n",
       " array([0.01764, 0.40067, 0.03682, 0.     , 0.02293, 0.52194, 0.     ]),\n",
       " array([0.02467, 0.38728, 0.05052, 0.00057, 0.39394, 0.07889, 0.06413]),\n",
       " array([0.35497, 0.02208, 0.04572, 0.00243, 0.47603, 0.08905, 0.00971]),\n",
       " array([0.00351, 0.41107, 0.03358, 0.00057, 0.45674, 0.09452, 0.     ]),\n",
       " array([0.02106, 0.38998, 0.10604, 0.00057, 0.43811, 0.02599, 0.01825]),\n",
       " array([0.1963 , 0.19763, 0.041  , 0.00059, 0.10397, 0.09189, 0.36862]),\n",
       " array([0.00351, 0.45219, 0.05575, 0.01199, 0.41565, 0.0609 , 0.     ]),\n",
       " array([0.38879, 0.     , 0.07583, 0.     , 0.01459, 0.06351, 0.45727]),\n",
       " array([0.00525, 0.40755, 0.11994, 0.     , 0.41417, 0.03944, 0.01365]),\n",
       " array([0.15303, 0.24469, 0.08054, 0.00293, 0.01404, 0.05071, 0.45406]),\n",
       " array([0.38712, 0.00221, 0.06155, 0.00061, 0.00487, 0.07639, 0.46725]),\n",
       " array([0.00703, 0.53049, 0.04906, 0.00685, 0.33358, 0.07299, 0.     ]),\n",
       " array([0.36001, 0.01543, 0.08844, 0.00061, 0.01455, 0.05052, 0.47044]),\n",
       " array([0.07467, 0.33196, 0.05098, 0.00058, 0.09707, 0.0796 , 0.36515]),\n",
       " array([0.     , 0.41719, 0.08382, 0.14283, 0.09178, 0.26438, 0.     ]),\n",
       " array([0.35121, 0.02649, 0.03   , 0.     , 0.41771, 0.10659, 0.068  ]),\n",
       " array([0.03   , 0.38166, 0.03846, 0.00057, 0.31659, 0.09049, 0.14223]),\n",
       " array([0.35485, 0.02207, 0.04713, 0.     , 0.2525 , 0.09037, 0.23308]),\n",
       " array([0.01054, 0.40289, 0.05106, 0.00057, 0.45688, 0.07805, 0.     ]),\n",
       " array([0.     , 0.41479, 0.08978, 0.     , 0.02281, 0.47262, 0.     ]),\n",
       " array([0.36037, 0.01545, 0.06925, 0.     , 0.33983, 0.06945, 0.14564]),\n",
       " array([0.17699, 0.2177 , 0.09943, 0.00176, 0.34747, 0.03456, 0.12208]),\n",
       " array([0.02289, 0.38917, 0.05252, 0.     , 0.43037, 0.07758, 0.02747]),\n",
       " array([0.36073, 0.01546, 0.05288, 0.00061, 0.46652, 0.08437, 0.01944]),\n",
       " array([0.00351, 0.44203, 0.07573, 0.00114, 0.03646, 0.0538 , 0.38735]),\n",
       " array([0.1273 , 0.27336, 0.06581, 0.     , 0.32633, 0.06734, 0.13986]),\n",
       " array([0.0945 , 0.30975, 0.06272, 0.00058, 0.44039, 0.06889, 0.02318]),\n",
       " array([0.00526, 0.40797, 0.08643, 0.00171, 0.45105, 0.04303, 0.00456]),\n",
       " array([0.09819, 0.30592, 0.05188, 0.00058, 0.246  , 0.07929, 0.21815]),\n",
       " array([0.01406, 0.39878, 0.06182, 0.00114, 0.43865, 0.06727, 0.01828]),\n",
       " array([0.00705, 0.40852, 0.03183, 0.08884, 0.00459, 0.45917, 0.     ]),\n",
       " array([0.01936, 0.39312, 0.05518, 0.00229, 0.35235, 0.07245, 0.10525]),\n",
       " array([0.33854, 0.03957, 0.07397, 0.00181, 0.00484, 0.06247, 0.4788 ]),\n",
       " array([0.     , 0.65882, 0.11727, 0.     , 0.18186, 0.02842, 0.01364]),\n",
       " array([0.02814, 0.38239, 0.09615, 0.00057, 0.10058, 0.03556, 0.35662]),\n",
       " array([0.19037, 0.20356, 0.06724, 0.     , 0.42425, 0.06744, 0.04714]),\n",
       " array([0.00351, 0.43365, 0.07384, 0.01541, 0.36061, 0.03994, 0.07304]),\n",
       " array([0.3265 , 0.05262, 0.09292, 0.00543, 0.25081, 0.04019, 0.23152]),\n",
       " array([0.37478, 0.     , 0.04944, 0.00061, 0.     , 0.08797, 0.48721]),\n",
       " array([0.00175, 0.41182, 0.08771, 0.00171, 0.44617, 0.04173, 0.00911]),\n",
       " array([0.36572, 0.00882, 0.09988, 0.     , 0.06792, 0.04043, 0.41722]),\n",
       " array([0.3597 , 0.01542, 0.10262, 0.     , 0.04361, 0.03769, 0.44096]),\n",
       " array([0.12532, 0.27505, 0.0931 , 0.00291, 0.     , 0.03814, 0.46548]),\n",
       " array([0.     , 0.41504, 0.10648, 0.     , 0.     , 0.47848, 0.     ]),\n",
       " array([0.00877, 0.40401, 0.09103, 0.     , 0.44669, 0.04495, 0.00456]),\n",
       " array([0.00526, 0.40828, 0.41048, 0.00171, 0.0228 , 0.15147, 0.     ]),\n",
       " array([0.37779, 0.00221, 0.04371, 0.00122, 0.     , 0.09271, 0.48236]),\n",
       " array([0.00176, 0.41335, 0.09193, 0.00057, 0.00457, 0.48782, 0.     ]),\n",
       " array([0.01054, 0.40258, 0.06378, 0.     , 0.38349, 0.06658, 0.07304]),\n",
       " array([0.00353, 0.41254, 0.21086, 0.19023, 0.     , 0.18284, 0.     ]),\n",
       " array([0.36806, 0.00662, 0.08287, 0.     , 0.07772, 0.05667, 0.40805]),\n",
       " array([0.33137, 0.0484 , 0.03274, 0.00061, 0.45014, 0.10286, 0.03388]),\n",
       " array([0.00526, 0.41739, 0.07443, 0.00228, 0.33739, 0.05383, 0.10942]),\n",
       " array([0.36492, 0.01543, 0.08417, 0.     , 0.00485, 0.05524, 0.47537]),\n",
       " array([0.00175, 0.41136, 0.10567, 0.00057, 0.191  , 0.0259 , 0.26376]),\n",
       " array([0.34528, 0.03309, 0.03211, 0.00182, 0.46585, 0.10244, 0.01941]),\n",
       " array([0.01936, 0.39321, 0.04779, 0.00057, 0.44397, 0.08137, 0.01373]),\n",
       " array([0.86175, 0.     , 0.05444, 0.00061, 0.     , 0.08321, 0.     ]),\n",
       " array([0.01232, 0.47476, 0.03971, 0.00801, 0.37073, 0.08073, 0.01373]),\n",
       " array([0.37059, 0.00442, 0.05725, 0.     , 0.00487, 0.08111, 0.48177]),\n",
       " array([0.00702, 0.40654, 0.06174, 0.00057, 0.45176, 0.06781, 0.00456]),\n",
       " array([0.     , 0.41971, 0.04701, 0.     , 0.0137 , 0.51958, 0.     ]),\n",
       " array([0.02293, 0.39431, 0.03237, 0.00287, 0.32554, 0.09361, 0.12838]),\n",
       " array([0.3681 , 0.00662, 0.08502, 0.00182, 0.     , 0.05263, 0.48581]),\n",
       " array([0.37275, 0.00221, 0.04942, 0.     , 0.39935, 0.08861, 0.08766]),\n",
       " array([0.00351, 0.71213, 0.04229, 0.     , 0.13695, 0.09599, 0.00913]),\n",
       " array([0.00176, 0.41321, 0.0309 , 0.00343, 0.45681, 0.0939 , 0.     ]),\n",
       " array([0.01408, 0.39943, 0.09436, 0.     , 0.     , 0.49213, 0.     ]),\n",
       " array([0.02111, 0.39085, 0.06524, 0.     , 0.28357, 0.06543, 0.1738 ]),\n",
       " array([0.01757, 0.41289, 0.07257, 0.00057, 0.42951, 0.05775, 0.00914]),\n",
       " array([0.34653, 0.03083, 0.06625, 0.     , 0.27611, 0.07199, 0.20829]),\n",
       " array([0.04776, 0.36168, 0.05276, 0.00057, 0.41854, 0.07729, 0.04139]),\n",
       " array([0.15665, 0.24045, 0.08743, 0.00117, 0.05618, 0.04616, 0.41196]),\n",
       " array([0.12194, 0.27973, 0.04799, 0.00058, 0.37764, 0.08353, 0.08858]),\n",
       " array([0.48703, 0.     , 0.04587, 0.00244, 0.0195 , 0.08936, 0.35581]),\n",
       " array([0.00176, 0.44099, 0.04074, 0.     , 0.00457, 0.51194, 0.     ]),\n",
       " array([0.     , 0.41613, 0.03891, 0.01488, 0.     , 0.53009, 0.     ]),\n",
       " array([0.02821, 0.3834 , 0.04854, 0.00057, 0.45842, 0.08086, 0.     ]),\n",
       " array([0.11647, 0.28589, 0.04316, 0.00058, 0.45191, 0.088  , 0.01398]),\n",
       " array([0.33169, 0.05268, 0.06462, 0.00121, 0.36698, 0.07176, 0.11106]),\n",
       " array([0.76532, 0.     , 0.03371, 0.00183, 0.01951, 0.1016 , 0.07803]),\n",
       " array([0.36678, 0.00885, 0.04937, 0.00061, 0.36977, 0.08785, 0.11677]),\n",
       " array([0.09982, 0.30334, 0.08655, 0.00116, 0.15293, 0.0457 , 0.3105 ]),\n",
       " array([0.00176, 0.41804, 0.01277, 0.     , 0.42967, 0.13776, 0.     ]),\n",
       " array([0.80592, 0.00221, 0.05656, 0.00061, 0.00974, 0.08114, 0.04382]),\n",
       " array([0.01228, 0.45945, 0.08854, 0.00171, 0.34666, 0.04118, 0.05017]),\n",
       " array([0.00876, 0.40809, 0.49545, 0.00057, 0.     , 0.08713, 0.     ]),\n",
       " array([0.09456, 0.30997, 0.0498 , 0.00058, 0.45926, 0.08118, 0.00464]),\n",
       " array([0.00701, 0.42432, 0.10792, 0.00228, 0.38744, 0.06191, 0.00912]),\n",
       " array([0.00702, 0.41114, 0.07047, 0.0057 , 0.42898, 0.05388, 0.02282]),\n",
       " array([0.0035 , 0.43728, 0.09164, 0.00285, 0.25508, 0.04568, 0.16398]),\n",
       " array([0.20763, 0.18511, 0.039  , 0.     , 0.03315, 0.09471, 0.4404 ]),\n",
       " array([0.36805, 0.00662, 0.08358, 0.     , 0.17001, 0.056  , 0.31574]),\n",
       " array([0.08543, 0.3197 , 0.06601, 0.00058, 0.09717, 0.06555, 0.36556]),\n",
       " array([0.09069, 0.31776, 0.09927, 0.00116, 0.30515, 0.03339, 0.15258]),\n",
       " array([0.00175, 0.43942, 0.14659, 0.00057, 0.17312, 0.23399, 0.00456]),\n",
       " array([0.36273, 0.01762, 0.10049, 0.00303, 0.12116, 0.03635, 0.35863]),\n",
       " array([0.2304 , 0.15992, 0.04405, 0.00238, 0.09509, 0.08782, 0.38035]),\n",
       " array([0.     , 0.43839, 0.03939, 0.     , 0.01828, 0.50394, 0.     ]),\n",
       " array([0.32056, 0.05913, 0.08643, 0.0006 , 0.4384 , 0.05152, 0.04336]),\n",
       " array([0.09276, 0.31201, 0.05252, 0.0029 , 0.21799, 0.07601, 0.24581]),\n",
       " array([0.0158 , 0.39621, 0.09194, 0.00228, 0.4518 , 0.0374 , 0.00456]),\n",
       " array([0.51599, 0.     , 0.05086, 0.     , 0.     , 0.08728, 0.34587]),\n",
       " array([0.08891, 0.31522, 0.09315, 0.00058, 0.44846, 0.03981, 0.01387]),\n",
       " array([0.35028, 0.02642, 0.07765, 0.     , 0.25674, 0.06123, 0.22768]),\n",
       " array([0.12193, 0.27972, 0.05485, 0.0035 , 0.34032, 0.07381, 0.12587]),\n",
       " array([0.1091 , 0.2938 , 0.05266, 0.     , 0.36271, 0.07944, 0.1023 ]),\n",
       " array([0.00351, 0.46559, 0.17144, 0.     , 0.10955, 0.24535, 0.00456]),\n",
       " array([0.38026, 0.00442, 0.06153, 0.00061, 0.09731, 0.07636, 0.37951]),\n",
       " array([0.007  , 0.40524, 0.12683, 0.     , 0.44577, 0.01516, 0.     ]),\n",
       " array([0.37586, 0.00883, 0.08643, 0.00364, 0.     , 0.04925, 0.47599]),\n",
       " array([0.36205, 0.01989, 0.03075, 0.00061, 0.37439, 0.10535, 0.10697]),\n",
       " array([0.00702, 0.41103, 0.06709, 0.00171, 0.37412, 0.06147, 0.07756]),\n",
       " array([0.29899, 0.08289, 0.09527, 0.003  , 0.42708, 0.03999, 0.05279]),\n",
       " array([0.11088, 0.29167, 0.06564, 0.00291, 0.41848, 0.06393, 0.0465 ]),\n",
       " array([0.00176, 0.41277, 0.05208, 0.     , 0.44264, 0.09076, 0.     ]),\n",
       " array([0.36801, 0.00662, 0.08571, 0.     , 0.06314, 0.05397, 0.42255]),\n",
       " array([0.00699, 0.40961, 0.50307, 0.     , 0.07275, 0.00758, 0.     ]),\n",
       " array([0.10542, 0.29774, 0.05807, 0.     , 0.04181, 0.0742 , 0.42275]),\n",
       " array([0.00701, 0.41978, 0.07977, 0.     , 0.32364, 0.05128, 0.11852]),\n",
       " array([0.0035 , 0.40944, 0.1037 , 0.     , 0.43218, 0.02843, 0.02275]),\n",
       " array([0.37555, 0.00443, 0.05369, 0.00061, 0.     , 0.08383, 0.48189]),\n",
       " array([0.00351, 0.41091, 0.04163, 0.00114, 0.452  , 0.08624, 0.00457]),\n",
       " array([0.37501, 0.     , 0.04302, 0.00305, 0.09263, 0.09141, 0.39489]),\n",
       " array([0.     , 0.4696 , 0.0557 , 0.00913, 0.02738, 0.06401, 0.37419]),\n",
       " array([0.02464, 0.38688, 0.06797, 0.     , 0.11898, 0.06292, 0.33862]),\n",
       " array([0.73627, 0.03528, 0.05279, 0.01273, 0.04851, 0.07075, 0.04366]),\n",
       " array([0.34647, 0.03082, 0.0698 , 0.     , 0.36323, 0.06861, 0.12108]),\n",
       " array([0.00352, 0.41585, 0.04704, 0.00057, 0.02742, 0.5056 , 0.     ]),\n",
       " array([0.25512, 0.13231, 0.05544, 0.00239, 0.01909, 0.07754, 0.45811]),\n",
       " array([0.00706, 0.40891, 0.09477, 0.06713, 0.02295, 0.39919, 0.     ]),\n",
       " array([0.17537, 0.22007, 0.07189, 0.00059, 0.44186, 0.06202, 0.0282 ]),\n",
       " array([0.06195, 0.34976, 0.0873 , 0.     , 0.04602, 0.04538, 0.40958]),\n",
       " array([0.36401, 0.01103, 0.08993, 0.00182, 0.04853, 0.04786, 0.43681]),\n",
       " ...]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
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